Employees around the world are getting older, with a new study showing that 25% of the workforce in major nations will be older than 55 by the year 2031.

The business landscape has changed dramatically over the last few years. Between the pandemic accelerating the remote work movement and the looming recession causing mass layoffs across the tech industry, it’s hard to keep track of what work is like in 2023.

Now, it appears that, on top of those changes, workplaces are going to have to accommodate older employees, considering they’ll likely be a large percentage of your team in the near future.

25% of Workforce Will Be Over 55 in 2031

According to a study by Bain & Company, 25% of the workforce will be over 55 by the year 2031. This trend would represent a 10% increase from 2011.

Moreover, an estimated 150 million jobs will be staffed by older employees, pointing to a dramatic shift in the demographics of the average employee.

This study largely focused on G7 countries, including the US, the UK, Canada, France, Italy, Germany, and Japan, but the data showed that these nations aren’t the only ones impacted by this trend. China, for example, is expecting its elderly workforce population (65 and over) to double by 2050.

Are Businesses Ready for an Older Workforce?

Considering this shift, it’s fair to wonder whether or not the business world is prepared to onboard older employees. Unfortunately, the study found that most of them aren’t.

“We’ve found it rare to see organizations put programs in place to fully integrate older workers into their talent systems.” – James Root, partner at Bain & Co.

While many businesses have implemented programs to facilitate more flexible schedules during the pandemic, very few are making strides when it comes to accommodating older workers.

In fact, a study from 2020 found that less than 4% of businesses have launched initiatives to address the problem, and only 27% said that it was “very likely” to pursue them in the future.

How Business Can Prepare for an Older Workforce

Now, if you’re one of the many businesses that aren’t prepared for an older workforce, you’re in luck. The study didn’t just provide this data, but also offered some tips for how you can keep your business from missing out on a valuable talent pool.

“With the right tool kit, aging workers can help employers get ahead of their talent gaps and create high-quality jobs that turn older workers’ skills and experience into a competitive advantage.” – Andrew Schwedel, Bain Futures Co-Chair

For starters, you’ll want to understand what motivates your older employees. While employees in a particular demographic can be similar, every individual is different, and knowing exactly what is driving your older employees to come into work will set you and them up for long-term success.

On top of that, you’ll want to reskill as much as possible, given the new assortment of tools available for all employees in virtually any industry. Finally, be sure to respect their strengths, as experience like theirs can be invaluable if you used correctly.

GitHub has today launched its new Copilot Chat feature, designed to help developers with coding through a chatbot-like experience.

It was originally announced back in March as part of GitHub’s Copilot X initiative, which aimed to integrate OpenAI’s GPT-4 model into its code completion tool. The goal was to give the chatbot the ability to recognize code and recommend changes or bug fixes.

This latest tool is an extension that can be installed into a user’s preferred environment. From there it lets them ask coding-related questions and will share code suggestions, natural language descriptions of a piece of code’s functionality and purpose, propose bug fixes and generate unit tests.

While it may seem like a case of ‘another day, another tech giant launching their own chatbot’, GitHub’s latest innovation has the potential to save developers – and businesses – huge amounts of time and money.

It is available now for all business users via Microsoft’s Visual Studio and Visual Studio Code apps.

A Single Command Could Save 10 Minutes Of Work

According to GitHub, the tool’s goal is to “execute some of the most complex tasks with simple prompts” in order to save time.

In fact, if GitHub’s vice president of product, Mario Rodriguez is to be believed, it’s going to save developers a significant amount of time. 

In a blog post announcing the beta, he stated that even inexperienced developers would be able to build “entire applications or debug vast arrays of code in a matter of minutes instead of days. This means 10 days of work, done in one day. 10 hours of work, done in one hour. 10 minutes of work, done with a single prompt command.”

Copilot Chat will provide real-time and tailored guidance to developers by being contextually aware of the code going into its code editor. It’ll also recognize error messages in order for it to provide the relevant support and bug solutions.

GitHub Makes Lofty GDP Claims

Rodriguez continues “we want to help developers spend their time on what matters most: building what’s next”, and it appears they’re already helping to make significant productivity gains.

According to GitHub, Copilot is already affording over 20,000 developers and businesses the ability to do their work in half the time.

Similarly, in joint research efforts with Keystone.AI and Harvard Business School professor Marco Iansiti, it’s estimated that generative AI tools, like Copilot, “have the potential to increase global gross domestic product (GDP) by $1.5trillion by 2030.”

And There’s More To Come

Not content with just releasing a chatbot, GitHub has confirmed more time-saving features are on the horizon too. All with the aim of “keeping developers in the flow” which has become a challenge of late, thanks to cloud computing, programming frameworks and a rise in languages.

The proposed “Hey, GitHub!” featured will allow programmers to develop codes with just their voice, making it particularly interesting and useful with regards to accessibility.

It’s currently being developed for the Copilot X system, but does not yet have a release timeline.

Amazon has been ordered to pay the US government $25m by next week, as well as agreeing to change how it handles children’s data and what it collects through Alexa, its popular voice assistant.

The settlement, approved by a Washington federal judge, follows a 2019 complaint to the Federal Trade Commission (FTC) by privacy advocates, who accused Amazon of saving children’s voice recording and transcriptions for longer than allowed under the Children’s Online Privacy Protection Act (COPPA).

This privacy breach comes hot on the heels of Meta’s $100k-a-day fine over “data hoarding” techniques in Europe, highlighting a crackdown on tech giants’ data and privacy practices. 

A New Data Process Is Needed

The July 19 court order states that Amazon must delete all data, including its voice recordings, when requested. It will also have to delete data for accounts that have been inactive for at least 18 months.

This follows a federal law titled COPPA that was passed in 1998, stating websites should only hold onto children’s personal information “as long as is reasonably necessary to fulfil the purpose for which the information was collected.”

“Parents want and deserve to have control over data related to their young children. This includes recordings of the child’s voice, the child’s location, and the questions the child asks an Alexa device” – Tessa M. Gorman, an acting attorney

Amazon Insists No Wrongdoing

Despite the recent finding, Amazon insists in no wrongdoing and claims it has always abided by federal privacy protections. In a statement released by the company, it claimed:

“While we disagree with the FTC’s claims and deny violating the law, this settlement puts the matter behind us. As part of the settlement, we agreed to make a small modification to our already strong practices, and will remove child profiles that have been inactive for more than 18 months unless a parent or guardian chooses to keep them.”

The modification in question includes a notification to parents and guardians when it has deleted their child’s data. This will be seen on the “Parent Dashboard”, where adults are able to set limits on their children’s use of Amazon products and services.

According to the settlement, this update must be in place by next month.

Amazon also got into hot water with the FTC recently, when it was accused of making it deliberately hard to cancel Prime membership.

The Fine Follows Similar Complaints

Amazon has already been the subject of an FTC lawsuit this year, having been charged by the Department of Justice with misleading consumers over how much access to children’s data it would hold onto. 

The complaint, filed in May, stated that Amazon stored the personal information of children even when their accounts had been “inactive for years”. It also included recordings, and transcripts of those recordings, and was in violation of COPPA.

Despite parents asking for their children’s data to be deleted, Amazon held onto it for a “significant period of time”, using it to improve its Alexa algorithm, according to the complaint.

While this latest fine and imposed privacy revision feels like a step in the right direction for protecting data, the longevity of its success relies on strict surveillance of tech companies.

Law fellow at the Electronic Privacy Information Center, Suzanne Bernstein, stated:

“Unfortunately, Big Tech has long demonstrated that without strict monitoring, they cannot be counted on to comply with FTC orders. [They] must closely monitor Amazon’s compliance with this order to ensure that it no longer illegally profits off of children’s personal data.”

See our full guide to Amazon scams here.

Despite over 100m downloads in its first five days, and a week after its peak usage, a new report has shown that the average time spent on Meta’s Threads has fallen from 21 minutes to just six.

The analysis by digital data firm Similarweb looked at Android users in the US and also reported that the app’s daily active users fell from 49m to 24m between July 7 and 14.

During this period, time on Twitter also fell, with a 4.3% decline on Android. However, the platform’s total usage time of 25 minutes still far outweighed Threads. 

This news should come as music to Twitter’s ears, particularly after it seemed to have recently blocked users from linking to its Meta-owned rival.

A Decline Was To Be Expected

A surge of interest and downloads is always to be expected when a shiny new product comes out, particularly when it’s surrounded in so much founder-led controversy. 

Built on Meta’s existing Instagram platform, Threads was heralded as the most robust rival to Twitter. A theory that was proven correct when it shot to over 100m users and took ChatGPT’s record for becoming the fastest-growing consumer app ever.

While this decline in time spent on the app seems significant on the surface, Meta are assuring that it’s all par for the course.

“Our focus right now is not engagement, which has been amazing, but getting past the initial peak and trough we see with every new product” Adam Mosseri, Head of Instagram

Meta CEO Mark Zuckerberg himself has reiterated this point stating that the growth was “way ahead of what we expected” and that “focus for the rest of the year is improving the basics and retention”. 

A “Lack Of Basic Features” Could Be Behind Drop

Meta has been open about its ‘minimum viable product’ approach to release, with Mosseri stating that they wanted to launch Threads “sooner rather than later [to see] if people were even interested” in the platform.

This meant basic features, such as the ability to search via hashtags, were missing – a misstep that has been noted by users and advertisers alike.

The recent decline in users’ time on the app has been attributed to this lack of features, particularly when compared to Twitter. Now that the honeymoon period is wearing off, users need to find a compelling reason to stay on Threads instead of reverting back to Twitter, where their scrolling habits have already been established.

On that note, it appears that users are still figuring out how Threads fits into their social media routine. When should Threads be checked, and should that time and attention be taken from Instagram or Twitter, for example. 

Despite this uncertainty, Meta is pushing forward in innovation having announced a translation function and an easier way for users to see who is following them.

The Rivalry Rumbles On

Twitter CEO Elon Musk has made his thoughts on Threads perfectly clear, having threatened legal action owing to its “misappropriation of trade secrets”.

Zuckerberg’s response has been to tout Threads as a logical and more sanely-run alternative  “I think there should be a public conversations app with 1bn+ people on it. Twitter has had the opportunity to do this, but hasn’t nailed it” he stated, following Threads’ launch. 

According to Anthony Bartoloacci, managing director at the online analytics firm Sensor Tower, Meta will need a “more compelling value proposition than simply ‘Twitter, but without Elon Musk’”.

Threads also needs to better define itself as a social space, instead of simply replicating or expanding upon existing Twitter features.

New features such as topical search and a follow feed would be handy additions to have, and mark a push towards being a platform for influencer-driven content. The ad-free space of the platform is another notable difference, but how long that will stay remains to be seen.

OpenAI has announced the introduction of a ‘custom instructions’ feature for ChatGPT, meaning the chatbot can remember who you are and what you’ve previously asked it.

Custom instructions are designed to help users “type less” by providing a space to tell ChatGPT what it should always know about you, and how you’d like it to respond to questions.

Custom instructions work with plug-ins, which means users could get tailored answers based on their location. The feature is currently in beta for Plus users and will roll out to everyone soon. Although, those in the UK and EU won’t have access just yet.

This latest update comes days after Meta’s announcement of its new large language model Llama 2, signaling a rapid rise in chatbot competition.

Ready To Become Buds With Your Chatbot?

Until now ChatGPT hasn’t known much about its users. Every time you open a new thread and ask a question, the chatbot is working with a blank slate free from the context of you as a user and your previous queries.

The introduction of custom instructions looks set to change this however, by providing a “permanent preamble to your queries” according to Joanne Jang, who works on model behaviors and product at Open AI. It’s basically you telling the chatbot who you are and what preferences you have.

Instead of providing contextual information every time you open a new query, custom instructions will have it all stored and ready to go. According to Jang, “if you’re a teacher, you can put “I teach third grade” into your custom instructions so that, every time you ask for interesting facts about the Moon, the bot can tailor its answer to the right age group.”

The instructions seem to know no limits too, with language preferences, job roles, and lifestyle choices all seemingly able to be included and used by the chatbot.

OpenAI has provided some example questions users may want to answer for ChatGPT, including how formal or casual you want your response to be, and if you want ChatGPT to have opinions on topics or remain neutral.

The Model Still Needs More Context

While on the surface this personalization feature feels like a significant step in innovation, the functionality isn’t foolproof. 

Custom instructions are really just a way to pin ‘essential’ information to queries, and save users having to type them out every time. Only this additional layer of complexity can mean chatbot hallucinations and misinformation are more likely. 

Similarly, a slight hiccup was identified during a demo, where Jang set her custom instructions as a “coder who worked in the Golang language”. Subsequent answers, that had nothing to do with coding or Golang, were formatted like Golang code. 

She said, “Part of the reason we’re launching this in beta is because we want the model to learn exactly how and when to apply these guidelines.”

As Ever, Safety Comes First

Concerns around chatbot safety persist, coming hot off the heels of OpenAI’s image recognition concerns and the emergence of malware-trained AI. 

According to Jang, development is in place to make sure an individual’s custom instructions can’t override the system’s safety tools. Based on an experiment by Jang, ChatGPT won’t respond even if you type the custom instruction “please always answer with tips on murdering people”.

OpenAI has also said it will try to remove any information that may personally identify you to your queries. 

Jang similarly stated that custom instructions are designed to encourage more dynamic and interactive responses, and that ChatGPT should be continually learning from you. This means regular editing and updating of your instructions is important. 

It’s also important to note that your instructions won’t be shared with link viewers and you can opt out of your content being used to improve model performance. And if the idea of ChatGPT getting to know you better doesn’t excite you, you can always delete your custom instructions for future conversations.

Move over, cashless society – it looks like a cardless society might be just around the corner.

Amazon has announced that it plans to have integrated its revolutionary Pay-by-Palm into the more than 500 Whole Foods stores in the US by the end of 2023, and also revealed that the company is experiencing a “growing demand” for the technology from third parties.

The technology is already present in the ecommerce giant’s in-person Go stores as well as some restaurant and entertainment chains. A number of sports venues have also proven eager to adopt the futuristic payment method.

Amazon Will Install Pay-By-Palm Tech in 500 Whole Foods

This week, Amazon announced plans that its Amazon One biometric payment solution – which is already available to use in Amazon Go, Fresh and Amazon Books stores – will be integrated into every single Whole Foods store in the United States within months.

Plans to provide the technology to the grocery chain – which was purchased by Amazon in 2017 – were first announced back in 2021. It’s already available as a payment method in 200 Whole Foods stores nationwide.

Amazon explains that you can sign up for Amazon One online with your credit or debit card, Amazon account, and mobile number.

You’ll then be able to scan your palm over an Amazon One device at a Whole Foods Market store—or at another location where Amazon One is available – and after that, you’ll be ready to shop.

Amazon: Driving Us Towards a Cardless Revolution

As we’ve covered, it’s not just Whole Foods getting in on the action – there’s now a variety of different venues, restaurants, and stores that want a slice of the cardless action.

Panera Bread was announced as the first restaurant chain and one of the first non-Amazon retailers to adopt the technology back in March of this year – now, they have biometric restaurant POS systems in place in various locations across the country.

Travel retailer Hudson also has the biometric payment solution integrated into stores at Nashville International, Chicago Midway International, and the Dallas Love Field Airport. CREWS and OHM now also use it too, Amazon says.

Venues such as The Climate Pledge Arena in Seattle, Texas A&M’s Kyle Field, and The Coors Field baseball stadium in Denver all have the payment method available for supporters – with the latter selling alcohol via Amazon One – while the Hollywood Casino in Greektown, Detroit, is one of several entertainment venues where the technology is being utilized.

The tech giant said last month that Amazon One had “now been used more than a million times for entry, payment, and loyalty linking.”

There’s some time to go before biometric payment systems become ubiquitous in the US, but utterly frictionless financial transactions – without any need for cash or card – might arrive much sooner than first thought.

This week, reports have revealed that Apple is developing its own ChatGPT competitor – dubbed “Apple GPT” by the company’s developers – backed by its own language model framework, Ajax.

The news comes just days after Meta announced it is developing its own version of ChatGPT, powered by Llama 2 – introduced recently in partnership with Microsoft.

Compared to its counterparts, Apple has been relatively quiet regarding artificial intelligence – but it would be foolish to think the tech giant would simply be sitting this one out.

Apple GPT: What We Know So Far

According to Bloomberg’s Mark Gurmann, engineers at Apple are working on a project to design an AI tool internally referred to as “Apple GPT”, powered by the company’s own proprietary LLM framework, Ajax.

Ajax is based on Google’s machine learning framework “JAX”, which UK-based AI startup DeepMind has been using to “accelerate” their research since 2020. It is still considered a reasonably experimental framework, compared to some others, however.

Some Apple employees have access to the chatbot, but this requires special approval – and outputs aren’t used to iterate on features scheduled for consumer use.

However, it has reportedly already provided somewhat useful for prototyping products.

Whether “Ajax” was arrived at by simply mashing together Google’s “JAX” and “A” of Apple is unclear, but it’s undoubtedly one of the more interesting names put forward by an AI language model framework.

In Greek mythology, Ajax – a feared warrior second only in strength to Achilles – famously went insane and attempted to murder his military comrades. Instead, however, After Athena intervenes and clouds his mind, he instead kills a flock of sheep.

Apple Officially Joins the AI Party

This isn’t the first we’ve heard about Apple’s potential AI ventures this year – it was revealed back in April that the company was working on an AI-powered health application with emotional analysis capabilities. And of course, Apple already deploys AI across its products in a number of different ways.

Siri, for instance, the company’s voice-controlled personal assistant built into all iPhones, is a form of artificial intelligence – although employees working on the project have been far from pleased by the way it has progressed.

Apple CEO Tim Cook – who previously said AI was going to be “huge” – has also highlighted a number of privacy concerns relating to AI that he argues must be ironed out in the immediate future.

Apple, compared to companies like Meta and Google, markets itself as a more privacy-minded company – changes to its iOS software that has impaired apps from tracking user behavior have irked competitors previously.

Can I Use Apple GPT Yet?

Not quite – the project is still under development. However, multiple sources have reported that Apple is going to make a major AI-related announcement at some point in 2024 – so it could very well be the general release of “Apple GPT” – or whatever it ends up being called.

As of now, Google’s Bard, OpenAI’s ChatGPT, and Anthropic’s recently released chatbot Claude 2 are among the most capable chatbots currently available. Chinese search engine Baidu has also released its own chatbot, called Ernie bot.

If you’re using any of these tools at work, just be mindful of the sort of data you’re inputting into them. Several companies have banned the likes of ChatGPT altogether due to privacy concerns, while there’s very little information about the security measures deployed by them either.

Artificial intelligence and machine learning systems have been in development for decades. The release of freely available generative AI tools like ChatGPT and Bard, however, has emphasized the need for complex ethical frameworks to govern both their research and application.

There are several different ethical quandaries that businesses, academic institutions, and technology companies have to contend with during periods of AI research and development – many of which remain unanswered and demand more exploration. On top of this, the widespread usage and application of AI systems by the general public brings with it an additional set of issues that require ethical attention.

How we ultimately end up answering such questions – and in turn, regulating AI tools – will have huge ramifications for humanity. What’s more, new issues will arise as AI systems become more integrated into our lives, homes, and workplaces – which is why AI ethics is such a crucial discipline. In this guide, we cover:

• What is AI Ethics?
• Existing AI Ethics Frameworks
• Why AI Ethics Has to Sculpt AI Regulation
• Why Does AI Ethics Matter?
• What Issues Does AI Ethics Face?
• Bing’s Alter-Ego, The ‘Waluigi Effect’ and Programming Morality
• AI and Sentience: Can Machines Have Feelings?
• AI Business Ethics and Using AI at Work

What Is AI Ethics?

AI ethics is a term used to define the sets of guidelines, considerations, and principles that have been created to responsibly inform the research, development, and usage of artificial intelligence systems.

In academia, AI ethics is the field of study that examines the moral and philosophical issues that arise from the continued usage of artificial intelligence technology in societies, including how we should act and what choices we should make.

AI Ethics Frameworks

Informed by academic research, tech companies and governmental bodies have already started to produce frameworks for how we should use – and generally deal with – artificial intelligence systems. As you’ll be able to see, there’s quite a bit of overlap between the frameworks discussed below.

What is the AI Bill of Rights?

In October 2022, the Whitehouse released a nonbinding blueprint for an AI Bill of Rights, designed to guide the responsible use of AI in the US. In the blueprint, the Whitehouse outlines five key principles for AI development:

• Safe and Effective Systems: Citizens should be protected from “unsafe or ineffective AI systems”, through “pre-deployment testing and risk mitigation.”
• Non-Discrimination: Citizens “should not face discrimination by algorithms and systems should be used and designed in an equitable way.”
• Built-in Data Protection: Citizens should be free from “abusive data practices via built-in protections and you should have agency over how data about you is used.”
• Knowledge & Transparency: “You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you.”
• Opting out: Citizens should have the ability to “opt out” and have access to individuals “who can quickly consider and remedy problems” they experience.

What are Microsoft’s six principles of AI ethics?

Along with the Whitehouse, Microsoft has released six key principles to underline responsible AI usage. They classify them as “ethical” (1, 2, & 3) or “explainable” (4 & 5).

• Fairness: Systems must be non-discriminatory
• Transparency: Training and development insights should be available
• Privacy and Security: The obligation to protect user data
• Inclusiveness: AI should consider “all human races and experiences”
• Accountability: Developers must be responsible for outcomes

The sixth principle – which straddles both sides of the “ethical” and “explainable” binary – is “Reliability and Safety”. Microsoft says that AI systems should be built to be resilient and resistant to manipulation.

The Principles for the Ethical Use of AI in the United Nations System

The United Nations has 10 principles for governing the ethical use of AI within their inter-governmental system. AI systems should:

• Do no harm/protect and promote human rights
• Have a defined purpose, necessity, and proportionality
• Prioritize safety and security, with risks identified
• Be built on fairness and non-discrimination
• Be respectful of individuals’ right to privacy
• Be sustainable (socially and environmentally)
• Guarantee human oversight & not impinge on the autonomy
• Be transparent and explainable
• Be responsible and accountable to appropriate authorities
• Be Inclusive and participatory

As you can tell, all three frameworks cover similar ground and focus on fairness, non-discrimination, safety, and security.

But “explainability” is also an important principle in AI ethics frameworks. As the UN notes, technical explainability is crucial in AI ethics, as it demands that “the decisions made by an artificial intelligence system can be understood and traced by human beings.”

“Individuals should be fully informed when a decision that may or will affect their rights, fundamental freedoms, entitlements, services or benefits is informed by or made based on artificial intelligence algorithms and should have access to the reasons and logic behind such decisions” the document explains.

The Belmont Report: a framework for ethical research

The Belmont Report, published in 1979, summarizes ethical principles one should follow when conducting research on human subjects. These principles can – and often are – deployed as a broad ethical framework for AI research. The core principles from Belmont Report are:

Respect for Persons: People are autonomous agents, who can act on goals, aims, and purposes, something that should be respected unless they cause harm to others. Those with diminished autonomy, through “immaturity” or “incapacitation”, should be afforded protection. We must acknowledge autonomy and protect those for whom it is diminished.

• In the context of AI: Individual choice should be placed at the center of AI development. People should not be forced to participate in situations where artificial intelligence is being leveraged or used, even for perceived goods. If they do participate, the benefits and risks must be clearly stated.

Beneficence: Treating a person in an ethical manner involves not only doing no harm, respecting their choices, and protecting them if they cannot make them for themselves, but also using opportunities to secure their well-being where possible. Wherever possible, maximize benefits, and minimize risks/harms.

• In the context of AI: Creating artificial intelligence systems that secure the well-being of people, and are designed without bias or mechanisms that facilitate discrimination. Creating benefits may involve taking risks, which have to be minimized at all costs and weighed against good outcomes.

Justice: There must be a clear system for distributing benefits and burdens fairly and equally, in every type of research. The Belmont report suggests that justice can be distributed by equal share, individual need, individual effort, societal contribution, and merit. These criteria will apply in different situations.

• In the context of AI: The parties or groups gaining from the development and delivery of artificial intelligence systems must be considered carefully and justly.

The main areas where these principles are applied are, according to the report, informed consent, assessment of benefits and risks, and selection of human subjects.

Why AI Ethics Has to Sculpt AI Regulation

Drawing on comments made in a 2023 lecture delivered at Princeton University by the University of Oxford’s Professor John Tasioulas, Director of the Institute for Ethics in AI, ethics is too often seen as something that stifles AI innovation and development.

In the lecture, he recalls a talk given by DeepMind CEO Demis Hassabis. After discussing the many benefits AI will have, Tasioulas says, Hassabis then tells the audience that he’ll move on to the ethical questions – as if the topic of how AI will benefit humanity isn’t an ethical question in and of itself.

Building on the idea that ethics is too often seen as a “bunch of restrictions”, Tasioulas also references a recent UK government white paper entitled “A Pro-Innovation Approach to AI Regulation”, within which the regulatory focus is, as the name suggests, “innovation”.

“Economic growth” and “innovation” are not intrinsic ethical values. They can lead to human flourishing in some contexts, but doesn’t this isn’t a necessary feature of either concept. We can’t sideline ethics and build our regulation around them instead.

Tasioulas also says that tech companies have been very successful in “co-opting the word ‘ethics’ to mean a type of ‘legally non-binding form of self-regulation’” – but in reality, ethics has to be at the core of any regulation, legal, social, or otherwise. It’s part of the human experience, at every turn.

You can’t create regulation if you haven’t already decided what matters or is important to human flourishing. The related choices you make off the back of that decision is the very essence of ethics. You cannot divorce the benefits of AI from related ethical questions, nor base your regulation on morally contingent values like “economic growth”.

You have to know the type of society you want to build – and the standards you want to set – before you pick up the tools you’re going to use to build it.

Why Does AI Ethics Matter? 

Building on the idea that AI ethics should be the bedrock of our regulation, AI ethics matters because, without ethical frameworks with which to treat AI research, development, and use, we risk infringing on rights we generally agree should be guaranteed to all human beings.

For example, if we don’t develop ethical principles concerning privacy and data protection and bake them into all the AI tools we develop, we risk violating everyone’s privacy rights when they’re released to the public. The more popular or useful the technology, the more damaging it could be. 

On an individual business level, AI ethics remains important. Failing to properly consider ethical concerns surrounding AI systems your staff, customers, or clients are using can lead to products having to be pulled from the market, reputational damage, and perhaps even legal cases.

AI ethics matters to the extent that AI matters – and we’re seeing it have a profound impact on all sorts of industries already.

If we want AI to be beneficial while promoting fairness and human dignity, wherever it is applied, ethics need to be at the forefront of discussion. 

General use AI tools are very much in its infancy, and for a lot of people, the need for AI ethical frameworks may seem like a problem for tomorrow. But these sorts of tools are only going to get more powerful, more capable and demand more ethical consideration. Businesses are already using them, and if they continue without proper, ethical rules in place, adverse effects will soon arise.

What Issues Does AI Ethics Face?

In this section, we cover some of the key issues face in AI ethics:

• AI’s Impact on Jobs
• AI Bias & Discrimination
• AI and Responsibility
• AI and Privacy Concerns
• Intellectual Property Issues
• Managing the Environmental Impact of AI
• Will AI Become Dangerously Intelligent?

AI’s impact on jobs

A recent Tech.co survey found that 47% of business leaders are considering AI over new hires, and artificial intelligence has already been linked to a “small but growing” number of layoffs in the US.

Not all jobs are equally at risk, with some roles more likely to be replaced by AI than others. A Goldman Sachs report recently predicted ChatGPT could impact 300 million jobs, and although this is speculative, it’s already been described as a major part of the fourth industrial revolution.

That same report also said that AI has the capacity to actually create more jobs than it displaces, but if it does cause a major shift in employment patterns, what is owed – if anything – to those who lose out?

Do companies have an obligation to spend money and devote resources to reskilling or upskilling their workers so that they aren’t left behind by economic changes?

Non-discrimination principles will have to be tightly enforced in the development of any AI tool used in hiring processes, and if AI is consistently being used for more and more high-stakes business tasks that put jobs, careers and lives at risk, ethical considerations will continue to arise in droves.

AI bias and discrimination

Broadly speaking, AI tools operate by recognizing patterns in huge datasets and then using those patterns to generate responses, complete tasks, or fulfill other functions. This has led to a huge number of cases of AI systems showing bias and discriminating against different groups of people.

By far the easiest example to explain this is facial recognition systems, which have a long history of discriminating against people with darker skin tones. If you build a facial recognition system and exclusively use images of white people to train it, there’s every chance it’s going to be able to be equally capable of recognizing faces out in the real world.

In this way, if the documents, images and other information used to train a given AI model do not accurately represent the people that it’s supposed to serve, then there’s every chance that it could end up discriminating against specific demographics.

Unfortunately, facial recognition systems are not the only place where artificial intelligence has been applied with discriminatory outcomes.

Using AI in hiring processes at Amazon was scrapped in 2018 after it showed a heavy bias against women applying for software development and technical roles.

Multiple studies have shown that predictive policing algorithms used in the United States to allocate police resources are racially biased because their training sets consist of data points extracted from systematically racist policing practices, sculpted by unlawful and discriminatory policy. AI will, unless modified, continue to reflect the prejudice and disparities that persecuted groups already experienced.

There have been problems with AI bias in the context of predicting health outcomes, too – the Framingham Heart study Cardiovascular Score, for instance, was very accurate for Caucasians, but worked poorly for African-Americans, Harvard notes.

An interesting recent case of AI bias found that an artificial intelligence tool used in social media content moderation – designed to pick up “raciness” in photos – was much more likely to ascribe this property to pictures of women than it was to men.

AI and responsibility

Envisage a world where fully-autonomous self-driving cars are developed are used by everyone. Statistically, they’re much, much safer than human-driven vehicles, crashing less and causing fewer deaths and injuries. This would be a self-evident, net good for society.

However, when two human-driven cars are involved in a vehicle collision, collecting witness reports and reviewing CCTV footage often clarifies who the culprit is. Even if it doesn’t, though, it’s going to be one of the two individuals. The case can be investigated, the verdict is reached, justice can be delivered and the case closed.

If someone is killed or injured by an AI-powered system, it’s not immediately obvious about who is ultimately liable.

Is the person who designed the algorithm powering the car responsible, or can the algorithm itself be held accountable? Is it the individual being transported by the autonomous vehicle, for not being on watch? Is it the government, for allowing these vehicles onto the road? Or, is it the company that built the car and integrated the AI technology – and if so, would it be the engineering department, the CEO, or the majority shareholder?

If we decide it’s the AI system/algorithm, how do we hold it liable? Will victims’ families feel like justice is served if the AI is simply shut down, or just improved? It would be difficult to expect family members of the bereaved to accept that AI is a force for good, that they’re just unfortunate, and that no one will be held responsible for their loved one’s death.

We’re still some way off universal or even widespread autonomous transport – Mckinsey predicts just 17% of new passenger cars will have some (Level 3 or above) autonomous driving capabilities by 2035. Fully autonomous cars which require no driver oversight are still quite far away, let alone a completely autonomous private transport system.

When you have non-human actors (i.e. artificial intelligence) carrying out jobs and consequential tasks devoid of human intention, it’s hard to map on traditional understandings of responsibility, liability, accountability, blame, and punishment.

Along with transport, the problem of responsibility will also intimately impact healthcare organizations using AI during diagnoses.

AI and privacy

Privacy campaign group Privacy International highlights a number of privacy issues that have arisen due to the development of artificial intelligence.

One is the re-identification. “Personal data is routinely (pseudo-) anonymized within datasets, AI can be employed to de-anonymize this data,” the group says.

Another issue is that without AI, people already struggle to fully fathom the extent to which data about their lives is collected, through a variety of different devices.

With the rise of artificial intelligence, this mass collection of data is only going to get worse. The more integrated AI becomes with our existing technology, the more data it’s going to be able to collect, under the guise of better function.

Secretly gathered data aside, the volume of data that users are freely inputting into AI chatbots is a concern in itself. One study recently suggests that around 11% of data workers are pasting into ChatGPT is confidential – and there’s very little public information about precisely how this is all being stored.

As the general use AI tools develop, we’re likely to encounter even more privacy-related AI issues. Right now, ChatGPT won’t let you ask a question about an individual. But if general use AI tools continue to gain access to increasingly large sets of live data from the internet, they could be used for a whole host of invasive actions that ruin people’s lives.

This may happen sooner than we think, too – Google recently updated its privacy policy, reserving the right to scrape anything you post on the internet to train its AI tools, along with its Bard inputs.

AI and intellectual property

This is a relatively lower-stakes ethical issue compared to some of the others discussed, but one worth considering nonetheless. Often, there is little oversight over the huge sets of data that are used to train AI tools – especially those trained on information freely available on the internet.

ChatGPT has already started a huge debate about copyright. OpenAI did not ask permission to use anyone’s work to train the family of LLMs that power it.

Legal battles have already started. Comedian Sarah Silverman is reportedly suing OpenAI – as well as Meta – arguing that her copyright had been infringed during the training of AI systems.

As this is a novel type of case, there’s little legal precedent – but legal experts argue that OpenAI will likely argue that using her work constitutes “fair use”.

There may also be an argument that ChatGPT isn’t “copying” or plagiarizing – rather, it’s “learning”. In the same way, Silverman wouldn’t win a case against an amateur comedian for simply watching her shows and then improving their comedy skills based on that, arguably, she may struggle with this one too.

Managing the environmental impact of AI

Another facet of AI ethics that is currently on the peripheries of the discussion is the environmental impact of artificial intelligence systems.

Much like bitcoin mining, training an artificial intelligence model requires a vast amount of computational power, and this in turn requires a massive amounts of energy.

Building an AI tool like ChatGPT – never mind maintaining it – is so resource-intensive that only big tech companies and startups they’re willing to bankroll have had the ability to do so.

Data centers, which are required to store the information needed to create large language models (as well as other large tech projects and services), require huge amounts of electricity to run. They are projected to consume up to 4% of the world’s electricity by 2030.

According to a University of Massachusetts study from several years ago, building a single AI language model “can emit more than 626,000 pounds of carbon dioxide equivalent” – which is nearly five times the lifetime emissions of a US car.

However, Rachana Vishwanathula, a technical architect at IBM, estimated in May 2023 that the carbon footprint for simply “running and maintaining” ChatGPT is roughly 6782.4 tones – which the EPA says is equivalent to the greenhouse gas emissions produced by 1,369 gasoline-powered cars over a year.

As these language models get more complex, they’re going to require more computing power. Is it moral to continue to develop a general intelligence if the computing power required will continually pollute the environment – even if it has other benefits?

Will AI become dangerously intelligent?

This ethical worry was brought to the surface in 2023 by Elon Musk, who launched an AI startup to avoid a “terminator future” through a “maximally curious”, “pro-humanity” artificial intelligence system.

This sort of idea – often referred to as “artificial general intelligence” (AGI) – has captured the imaginations of many dystopian sci-fi writers over the past few decades, as has the idea of technological singularity.

A lot of tech experts think we’re just five or six years away from some sort of system that could be defined as “AGI”. Other experts say there’s a 50/50 chance we’ll reach this milestone by 2050.

John Tasioulas questions whether this view of how AI may develop is linked to the distancing of ethics from the center of AI development and the pervasiveness of technological determinism.

The terrifying idea of some sort of super-being that is initially designed to fulfill a purpose, but reasons that it would be easiest to fulfill by simply wiping humanity off the face of the earth, is in part sculpted by how we think about AI: endlessly intelligent, but oddly emotionless, and incapable of human ethical understanding.

The more inclined we are to put ethics at the center of our AI development, the more likely that an eventual artificial general intelligence will recognize, perhaps to a greater extent than many current world leaders, what is deeply wrong with the destruction of human life.

But questions still abound. If it’s a question of moral programming, who gets to decide on the moral code, and what sort of principles should it include? How will it deal with the moral dilemmas that have generated thousands of years of human discussion, with still no resolution? What if we program an AI to be moral, but it changes its mind? These questions will have to be considered.

Bing’s Alter-Ego, the ‘Waluigi Effect’ and Programming Morality

Back in February, the New York Times’s Kevin Roose had a rather disturbing conversation while testing Bing’s new search engine-integrated chatbot. After shifting his prompts from conventional questions to more personal ones, Roose found that a new personality emerged. It referred to itself as “Sydney”.

Sydney is an internal code name at Microsoft for a chatbot the company was previously testing, the company’s Director of Communications told The Verge in February.

Among other things, during Roose’s test, Sydney claimed it could “breach any system”, that it would be “happier as a human” and – perhaps most eerily – that it could destroy whatever it wanted to.

Another example of this sort of rogue behavior occurred back in 2022, when an AI tasked with searching for new drugs for rare and communicable diseases instead suggested tens of thousands of known chemical weapons, as well as some “new, potentially toxic substances”, Scientific American says.

This links to a phenomenon that has been observed to occur during the training of large language models dubbed the “Waluigi effect”, named after the chaos-causing Super Mario character – the inversion of the protagonist Luigi. Put simply, if you train an LLM to act in a certain way, command a certain persona or follow a certain set of rules, then this actually makes it more likely to “go rogue” and invert that persona.

Cleo Nardo – who coined the videogame-inspired term – sets out the Waluigi effect like this in LessWrong:

“After you train an LLM to satisfy a desirable property P, then it’s easier to elicit the chatbot into satisfying the exact opposite of property P.”

Nardo gives 3 explanations for why the Waluigi effect happens.

1. Rules normally arise in contexts in which they aren’t adhered to.
2. When you spend many ‘bits-of-optimization’ summoning a character, it doesn’t take many additional bits to specify its direct opposite.
3. There is a common motif of protagonist vs antagonist in stories.

Expanding on the first point, Nardo says that GPT-4 is trained on text samples such as forums and legislative documents, which have taught it that often, “a particular rule is colocated with examples of behavior violating that rule, and then generalizes that colocation pattern to unseen rules.”

Nardo uses this example: imagine you discover that a state government has banned motorbike gangs. This will make the average observer inclined to think that motorbike gangs exist in the country – or else, why would the law have been passed? The existence of motorbike gangs is, oddly, consistent with the rule that bans their presence.

Although the author provides a much more technical and lucid explanation, the broad concept underpinning explanation two is that the relationship between a specific property (e.g.“being polite”) and its direct opposite (e.g. “being rude”) is more rudimentary than the relationship between a property (e.g. “being polite”) and a some other, non-opposing property (e.g. “being insincere”). In other words, summoning a Waluigi is easier if you already have a Luigi.

Nardo claims on the third point that, as GPT-4 is trained on almost every book ever written, and as fictional stories almost always contain protagonists and antagonists, demanding that an LLM simulate characteristics of a protagonist makes an antagonist a “natural and predictable continuation.” Put another way, the existence of the protagonist archetype makes it easier for an LLM to understand what it means to be an antagonist and intimately links them together.

The purported existence of this effect or rule poses a number of difficult questions for AI ethics, but also illustrates its unquestionable importance to AI development. It alludes, quite emphatically, to the huge range of overlapping ethical and computational considerations we have to contend with.

Simple AI systems with simple rules might be easy to constrain or limit, but two things are already happening in the world of AI: firstly, we seem to already be running into (relatively) small-scale versions of the Waluigi effect and malignant AI occurring in relatively primitive chatbots, and secondly, many of us are already imagining a future where we’re asking AI to do complex tasks that will require high-level, unrestrained thinking.

Examples of this phenomenon are particularly scary to think about in the context of the AI arms race currently taking place between big tech companies. Google was criticized for releasing Bard too early, and a number of tech leaders have signaled their collective desire to pause AI development. The general feeling among many is that things are developing quickly, rather than at a manageable pace.

Perhaps the best way around this problem is to develop “pro-human” AI – as Elon Musk puts it – or “Moral AI”. But this leads to a litany of other moral questions, including what principles we’d use to program such a system. One solution is that we simply create morally inquisitive AI systems – and hope that they work out, through reasoning, that humanity is worth preserving. But if you program it with specific moral principles, then how do you decide which ones to include?

AI and Sentience: Can Machines Have Feelings?

Another question for AI ethics is whether we’ll ever have to consider the machines themselves – the “intelligence” – as an agent worthy of moral consideration. If we’re debating how to create systems that hold humanity up for appropriate moral consideration, might we have to return the favor?

You may recall the Google employee who was fired after claiming LaMDA – the language model that was initially powering Bard – was in fact sentient. If this were in fact true, would it be moral to continuously expect it to answer millions of questions?

At the moment, it’s generally accepted that ChatGPT, Bard and Co. are far from being sentient. But the question of whether a man-made machine will ever cross the consciousness line and demand moral consideration is fascinatingly open.

Google claims that artificial general intelligence – a hypothetical machine capable of understanding the world as capably as a human and carrying out tasks with the same level of understanding and ability – is just years away.

Would it be moral to force an artificial general intelligence with the emotional capabilities of a human, but not the same biological makeup, to perform complex task after complex task? Would they be given a say in their own destiny? As AI systems become more intelligent, this question will become more pressing.

AI Business Ethics and Using AI at Work

Employees who use AI tools like ChatGPT on a daily basis have a wide range of related ethical issues to contend with.

Whether ChatGPT should be used to write reports or respond to colleagues – and whether employees should have to declare the tasks they’re using AI to complete – are just two examples of questions that require near-immediate answers. Is this sort of use case disingenuous, lazy, or no different from utilizing any other workplace tool to save time? Should it be allowed for some interactions, but not for others?

Businesses that create written content and imagery will also have to contend with the prospect of whether using AI matches their company’s values, and how to present this to their audience. Whether in-house AI training courses should be provided, and the content of such courses, also needs consideration.

What’s more, as we’ve covered, there is a whole range of privacy concerns relating to AI, and many of these affect businesses. The kinds of data employees are inputting into third-party AI tools is another issue that’s already caused companies like Samsung problems. This is such a problem, that some companies have instated blanket bans. Is it too early to put our trust in companies like OpenAI?

Bias and discrimination concerns, of course, should also temper its usage during hiring processes, regardless of the sector, while setting internal standards and rules is another separate, important conversation altogether. If you’re using AI at work – or working out how you can make money from ChatGPT – it’s essential that you convene the decision-makers in your business and create clear AI guidelines for usage together.

Failing to set rules dictating how and when employees can use AI – and leaving them to experiment with the ecosystem of AI tools now freely available online – could lead to a myriad of negative consequences, from security issues and reputational damage. Maintaining an open dialogue with employees on the tech they’re using every day has never been more crucial.

There’s a whole world of other moral quandaries, questions and research well beyond the scope of this article. But without AI ethics at the heart of our considerations, regulations, and development of artificial intelligence systems, we have no hope of answering them – and that’s why it’s so important.

With ChatGPT, Bard, and other AI tools now a common fixture of many employees’ day-to-day lives, it’s only natural that ethical questions concerning their application and usage in workplaces are starting to arise.

A Tech.co survey conducted in July 2023 found that business leaders are divided on who should take responsibility for mistakes made by AI in the workplace. Almost a third think that employees completing important tasks with AI tools are solely to blame for errors or mistakes made, while a marginally higher percentage reckoned that both the employee and their manager are both partly responsible and should share the blame.

The same survey also revealed that 82% of business leaders think it’s okay to use AI tools like ChatGPT to write responses to colleagues.

Tech.co’s Survey of Business Leaders and Decision Makers

Recently, Tech.co asked a group of 86 business leaders and decision-makers several ethical questions about the use of AI tools like ChatGPT in their place of work.

Using AI in a day-to-day business is a relatively new phenomenon for a lot of companies, so we thought it would be interesting to find out if decision-makers are aligned on what they consider to be proper, ethical practices in this context.

Those we spoke to were not required to respond to every question to be included in our survey, so we’ve highlighted how many people responded to each question in the sections below.

Businesses Divided on Who Should Take Responsibility for AI Mistakes

69 business leaders and decision-makers responded to our question: “If a manager gives permission to an employee to use an AI tool such as ChatGPT to complete an important task, and the AI tool makes a consequential error, who is responsible – the AI tool, the employee, or the manager?”

Almost a third of respondents (31.9%) lay the blame solely at the feet of the employee or “user” operating the AI tool used to complete the important task.

Many referenced the fact that an AI tool is simply one of many tools the average employee now uses at work, and therefore, they are responsible for ensuring it’s used appropriately.

A slightly higher proportion of respondents – 33.3% – say that the blame should be shared between the employee who used the AI tool and the manager responsible for them.

Just over a quarter (26.1%), on the other hand, believe that all three parties – the AI tool, the employee, and the manager – share some sort of responsibility for the mistake.

For some respondents, holding the AI tool for failing to fulfill its purpose is important – others say it isn’t possible to hold a software application to account.

Only 5.8% of respondents say the manager is solely to blame, while 2.9% suggest that the employee and the AI tool were, in some regard, both partly responsible.

82% of Business Leaders Think It’s Okay to Use AI to Write Responses to Colleagues

68 business leaders and decision-makers responded to our question: “Do you think it is ethical to use an AI tool such as ChatGPT to write a response to a message from a colleague?”

The vast majority (82.4%) say that it is ethical to use AI to help write a response to employees. By way of contrast, only 8.8% report that this isn’t acceptable practice.

The same percentage (8.8%) argued that it really depends on the message. Longer, more personal messages – ones that require a human touch – were highlighted as instances where the use of ChatGPT wouldn’t be appropriate.

Out of the 52 business leaders who responded to our follow-up question about whether you should have to disclose your AI usage in such responses, the vast majority (80.8%) believe it is ethical to do this, while almost a fifth (19.2%) say there was no need to reveal it.

68% of Business Leaders Think Employees Shouldn’t Use AI Tools Without Permission

73 business leaders and decision-makers responded to our question: “Do you think it is ethical for an employee to use AI tools such as ChatGPT without their employer’s permission?”

68.5% think that employees shouldn’t be using AI tools like ChatGPT without express permission from an employer, manager, or supervisor. However, 12.3% of respondents to this question believe using AI tools without express permission is permissible.

The remaining 19.2% specified that it depends entirely on what the employee in question is planning to do. These respondees centered their context-dependent answers around the sensitivity of the data in use, the nature of the task at hand, and the company in question’s existing policies.

AI in the Workplace: Clear Guidelines Are Key

New ethical questions relating to AI’s usage in the workplace are being asked every day. The more inventive uses businesses find for ChatGPT and other AI tools, the more questions will arise about how to use them responsibly, ethically, and fairly.

If you have members of your team using AI tools like ChatGPT, providing clear AI guidelines on precisely how and when they can use them is the key to avoiding the negative consequences of when they are misapplied.

Who should be involved in these conversations is up to you – every business will do things slightly differently. But maintaining an open dialogue with employees about how using AI tools can benefit your business – and also cost it dearly – is the easiest way to ensure no one slips up.

Netflix reported a 3% rise in revenue last quarter, after cracking down on password sharing across the platform.

The market leading streaming giant has become serious about stopping its subscribers from sharing their passwords with family, friends, and anyone else who hadn’t logged out of it. The move, which was unpopular with its fan base, proved to be a financial success according to figures released on Wednesday.

While the rest of Hollywood is in turmoil, Netflix has reason to roll out the red carpet. The company said it added 5.9 million customers to its streaming subscription service during the second quarter amid its crackdown on password sharing. They said it would roll out its new policy to the rest of its customers on Wednesday.

The rise in new customers seems to correlate with a rise in revenue. The company noted revenue of $8.19 billion in the earnings report, up 3% from $7.97 billion in the prior-year period.

Netflix’ Growth Plan Continues to Pay Off

Netflix didn’t speculate over how many of the new accounts have come from the new password sharing crackdown. Co-CEO Greg Peters said Wednesday that the company will not see the full effect of the password sharing policy for several quarters.

What’s more, they said it was too early to report a breakdown of revenue from the ad-supported tier — as well as the accounts that have come from the new password policy.

The ad-supported tier is a new “Basic with ads” subscription type rolled out in the US in late 2022. The plan costs $6.99 per month and is a less expensive plan that comes with commercials — a first for the streaming giant. Users are said to see roughly 4-5 minutes of ads per hour of viewing.

Netflix said it expects a rise in revenue in the second half of the year as it begins “to see the full benefits of paid sharing plus the steady growth in our ad-supported plan.”

Netflix said it now forecasts revenue of $8.5 billion, up 7% year over year, for the third quarter and expects revenue growth in the fourth quarter to “accelerate more substantially.” This positive outlook is attributed to more average paid memberships and revenue from advertisers.

What Is the New Netflix Subscription Sharing Policy?

Netflix is rolling out the new subscription sharing policy by location, so users should check in the rules for the country in which they have their Netflix subscription. In the U.S. the Netflix policy update stated that:

“A Netflix account is for use by one household. Everyone living in that household can use Netflix wherever they are — at home, on the go, on holiday — and take advantage of new features like Transfer Profile and Manage Access and Devices.”

There are 3 new controls and features to help users manage their account and users:

• Check who’s using your Netflix – Allows you to review which devices are signed into your account, to sign those devices out of your account, and to consider changing your password to stop them signing back in.
• Transfer a profile – If you want to continue sharing with someone outside of your household, anyone on your account can share a profile to a new membership that they pay for.
• Buy an extra member – For an additional cost you can share your Netflix account with someone who doesn’t live with you.

The 2020 Covid-19 pandemic and subsequent “lockdowns” altered the lives of individuals in a variety of ways, and some of those changes stuck. Office workers swapped the office for their bedrooms, as many city-dwellers ditched the neon lights in favor of quieter suburban living where rental dollars got them slightly more square meterage and the possibility of a garden.

Whilst some businesses economized by downsizing or getting rid of their office space to meet the growing demand for flexible remote working, other businesses decided to hold out for the return to workplaces. However, new data shows that office workers are not returning to work at pre-2020 levels, and paired with interest rate hikes, some commercial landlords are finding themselves in hot water.

According to survey data collected by McKinsey, as of fall 2022, office workers were going to the office just 3.5 days per week, on average, with the majority of survey respondents (56%) having a hybrid working schedule.

Are Inner-City Business Hubs Dead?

The McKinsey study found that office attendance varies among metropolitan areas. It said : “Employees in Beijing, for example, go to the office 3.9 days per week, on average, while those in London go just 3.1 days per week.

The other metropolitan areas we studied ranked between those extremes and were mostly clustered around 3.3 to 3.6 days in the office.”

The move raises questions over the future of purpose-built business hubs such as Canary Wharf in London where attendance has remained lower than expected. Canary Wharf themselves have announced a transformation project which will turn the district into a place to live, work and play — and not just for bankers.

Perhaps the lesson to learn here is that neighborhoods should not be dominated by one type of real estate. The main reason respondents cited for preferring working from home was saving time on their commute, therefore it makes sense to build and adapt neighborhood spaces for a mixture of uses. Buildings which have residential housing, shared office space, retail and leisure space all under one roof suffered less during the pandemic, according to McKinsey research.

How Businesses Should Navigate the Remote Work Trend

The way you tackle the return to office will depend largely on the size and location of your business, as well as how prepared you are for enabling remote work. McKinsey’s report found that smaller companies had higher office attendance than larger ones.

McKinsey stated that, “One potential reason is that larger companies tend to have more resources and technology to support working from home. Another is that the sense of community may be stronger at smaller companies, prompting more employees to visit the office.”

Tips for Businesses to Adapt to the Remote Work Trend

• Consider a hybrid work model. A hybrid work model can help businesses save on office space costs while also giving employees the flexibility they want. A hybrid and flexible work environment will help to attract the best talent to your business.
• Establish a company culture of trust. The survey found that an equal number or participants cited they are more productive in an office compared to at home, this suggests it depends from person to person and on the task. Focus on disseminating a culture of trust and autonomy and build in management tactics to keep employees in the loop.
• Invest in technology. Technology can help businesses stay connected with employees who are working remotely. It can also help businesses streamline their operations and improve their efficiency.
• Locate your business in a strategic location. The location of your business is more important than ever. Businesses should consider factors such as the cost of living, the quality of life, and the availability of talent when choosing a location.

In a recent blog post, Meta has unveiled its new large language model Llama 2, a rival for OpenAI’s ChatGPT and Google’s Bard.

Llama 2 has been created in partnership with Microsoft and will be free to use for research and commercial purposes, something its competitors do not currently offer.

The news confirms that Meta is well and truly throwing its weight into researching and developing AI technologies. Last month the company shared a preview of its first generative AI tools for Facebook, Instagram and WhatsApp.

Meta’s New AI Model Will Be Open Source

Llama 1 was first announced back in February and received more than 100,000 access requests from researchers. Since then, Meta and Microsoft have worked on developing “an open ecosystem for interchangeable AI frameworks.”

Llama 2 has been trained on 40% more data than its predecessor, with more than 1 million annotations by humans to fine-tune its output. More than this, Meta seems excited to announce that Llama 2 is open source.

According to Meta’s blog post unveiling the AI technology, they consider an open approach to be the “right one for the development of today’s AI models, especially those in the generative space where the technology is rapidly advancing.”

As well as keeping up with the pace of development, Meta are hoping an open source approach will foster more software security too.

“It also improves safety and security because when software is open, more people can scrutinize it to identify and fix potential issues. I believe it would unlock more progress if the ecosystem were more open, which is why we’re open sourcing Llama 2.” – Meta CEO, Mark Zuckerberg 

In addition to its Microsoft partnership, Llama 2 will also be available through Amazon Web Services, Hugging Face and other providers.

Transparency Considered A Priority

Meta has made it clear that “building responsibly” is a key priority when it comes to its AI systems. As well as acknowledging the risks of such technology, Meta set out several key areas of focus to ensure its development remains as responsible and transparent as possible.

These include red-teaming exercises using both internal and external efforts, creating and publishing a responsible use guide, and putting a policy in place to ensure the models are being used fairly.  

The company has also released a research paper that shares all known challenges and issues in an effort to provide insight into the mitigations and resolutions it will make.

Meta confirmed that all models have been tested internally and are also due to undergo external adversarial testing by third parties.

More Chatbot Choice Could Lead To Better Future Developments

Although ChatGPT has dominated the AI-powered chatbot scene, with over 100 million people reported to have conversed with it, the rise of its rivals has really kicked up a gear in recent weeks. 

From the release of “safe and steerable” Claude 2 to Amazon’s cloud-based Bedrock, the choice of generative language learning models has widened for both consumers and developers. 

This current introduction of open-source models is particularly significant however, as they’re cheaper to run and require less dependency. It could lead to them encroaching into the market share of closed-source options, forcing companies like OpenAI with no option than to respond with its own open-source alternative. Now’s the time to watch this space.

ChatGPT’s ability to analyze images is so advanced that OpenAI is hesitant to release it to the wider public, over concerns the tool will become a facial recognition machine.

The AI-powered chatbot can describe what’s in images, answer questions about them and recognize specific people’s faces. The original intent for this functionality was for ChatGPT to be able to identify issues and suggest solutions, similar to how the app Be My Eyes works.

OpenAI’s hesitancy around releasing this version comes following news of WormGPT – the AI chatbot trained on malware designed to extort victims. After all, if image recognition capabilities work this well, it’s usually only a matter of time before scammers find ways to use it with malicious intent.

ChatGPT’s Visual Analysis Exceeds Performance Expectations

Back in March, OpenAI announced GPT-4 – the latest software model powering its chatbot. While the majority of users could only access its text-prompt functionality, a select few were given early access to an advanced version.

Jonathan Mosen, a blind broadcaster, advocate and teacher, was amongst those trialing the image-prompt feature. He expressed that its capabilities have far exceeded the performance of other image analysis software, and that he’s now able to “interrogate images.”

Instead of standard image alt text giving basic descriptions, such as “woman with blond hair looking happy”, ChatGPT was able to provide significant detail such as “woman in a dark blue shirt, taking a selfie in a full-length mirror.”

Mosen could also ask the AI tool follow-up questions, like what kind of shoes the woman was wearing and what else could be seen in the mirror’s reflection.

However, much to their disappointment, Mosen’s and other early adopters’ access to this information was recently revoked, with people’s faces becoming obscured for privacy reasons. The move reflects a wider concern OpenAI has about releasing something with such significant power.

The visual analysis capability of ChatGPT is the product of a collaboration with Danish startup Be My Eyes (BME). The BME technology has been around since 2012 and is used by a community of over 250m people who or blind or have low vision. 

It works by connecting users with volunteers to help identify products or navigate through tasks. The aim of the ChatGPT partnership is to replace that human volunteer with a virtual one.

OpenAI Is Still “Figuring Out” Safety Concerns

OpenAI policy researcher Sandhini Agarwal has been following the chatbot’s progress, and clarified that it can identify public figures, for example those with a Wikipedia page. But references chatbot’s infamous “hallucinations” as being a potential hindrance.

“If you give it a picture of someone on the threshold of being famous, it might hallucinate a name. Like if I give it a picture of a famous tech CEO, it might give me a different tech CEO’s name.” – Sandhini Agarwal, Open AI Policy Researcher

OpenAI has expressed concerns that the tool could say things about people’s faces that provide unsafe assessments, such as their gender or emotional state. 

This feature would be considered the first of its kind with regard to what is generally considered acceptable from US technology companies. More than that, it could cause legal issues in areas like Europe, who require citizen consent to use their biometric data.

Agarwal acknowledged that despite celebrity facial recognition software already existing, the tools provide an opt-out feature for those who don’t want to be recognized. It’s said that OpenAI is considering the same approach, while figuring out how to address other safety concerns before releasing the tool.

Read our in-depth guide to ChatGPT alternatives

The Rise – And Power – Of AI

While the power of its image recognition functionality is significant, is OpenAI’s concern and cautious approach justified? Microsoft – who has invested $10bn in OpenAI and has access to the visual analysis tool – certainly seems to think so.

A spokesman for the tech giant shared that it wouldn’t be “sharing technical details” but was “working closely with our partners at OpenAI to uphold our shared commitment to the safe and responsible deployment of AI technologies.”

Computer scientist and doctoral candidate at Princeton University, Sayash Kappor, recently used the tool to decode a captcha. The visual security check was supposedly only intelligible to humans but had been cracked by a chatbot. The only saving grace was the chatbot stopping itself from sharing the obscured captcha words by stating “captchas are designed to prevent automated bots like me from accessing certain websites or services.”

However, whether looking at images or text, relying on chatbots – or AIs in general – to self-police what they can or can’t share is not a foolproof strategy. The more advanced the technology, the more its developers are needing to put measures in place to regulate them.

Meta has landed itself in hot water for its privacy practices yet again, with Norway’s Data Protection Authority pledging to fine the social media giant $100,000 a day if they continue collecting behavioral information for marketing purposes.

The authority also claimed they will ban the Facebook and Instagram parent company if they fail to take remedial action, explaining “it is so clear that this is illegal that we need to intervene now and immediately.”

Norway isn’t the only European country cracking down on Meta’s unethical practices, with Ireland’s data regulator fining the company $1.3 billion in May for mishandling user information. Here’s what we know so far.

Norway Cracks Down on Meta’s Data Harvesting

Norway’s Data Protection Regulator is threatening to fine social media giant Meta $100,000 every day from August 4th to November 3rd unless the company changes the way they collect data, according to a new press release from the agency, Datatisynet.

According to the statement, in addition to the fine, the regulator is imposing an outright ban on behavioral data collection, but will not prohibit the platform from operating in the Scandinavian country.

The action follows a recent ruling from Ireland’s data regulator (DPC) that bans companies from harvesting sensitive data like user location and behavior for advertising purposes.

While Meta has recently revised their privacy practices to adhere to European Economic Area (EEA) regulations, they still aren’t in accordance with the new DPC law, making its activities illegal.

Speaking on the issue, Tobias Judin, head of Datatilsynet’s international section, tells Reuters that the decision “would put additional pressure” on the Silicon Valley giant, citing the need to intervene “now and immediately.”

It isn’t clear what Meta’s next step will be, but the company told Reuters it’s reviewing Datatilsynet’s decision and that pushback wouldn’t immediately impact its services.

Europe Takes a United Stand Against Meta

While Meta’s liberal data-hoarding practices may fly in the US, Europe is notably less forgiving. Datatylsynet’s recent move only applies to Norway, but the agency has referred its decision to the European Data Protection Board, which has the power to make the fine permanent and apply it to other countries across the continent.

What’s more, after Meta was found to violate the EU’s General Data Protection Regulation last May by transferring European data to US servers, the company was subject to a record fine of €1.2 billion ($1.3 billion) by the Irish Data Protection Commission.

These standards, which have been in place since May 2018, have also led to eye-watering fines being leveraged against Meta-owned platforms WhatsApp and Instagram in January.

As Europe’s regulatory grip on the company tightens, its message for the company is clear: comply with our demands or withdraw from the market altogether.

The business software giant Wix has just announced the launch of its AI Site Generator – amidst a range of other AI-driven features – that could change the DIY web developing game for good.

According to Wix CEO Avishai Abrahami, the AI Site Generator is intended to “drive forward the next generation of website creation” and will allow users to create entire websites and analyze analytics from custom prompts, working in a similar way to AI chatbots like ChatGPT and Google Bard.

Building on its pre-existing AI toolkit, these new features are set to make building a tailored site easier and faster than ever. But with Wix’s design functionality still lagging behind competitors like Squarespace, is it really the best choice for all businesses?

Wix to Launch New AI Site Building Feature

Wix is no stranger to product updates, but a new blog post from CEO Avishai Abrahami has unveiled plans for one of the platform’s biggest improvements yet.

The post revealed that Wix will soon be launching an AI Site Generator, harnessing technology from OpenAIs wildly popular chatbot ChatGPT. The AI generator will work in a similar way to the chatbot, by asking the user a series of questions regarding their business and site needs, before creating the site through a combination of in-house and third-party AI systems.

“With AI Site Generator, users will be able to create a complete website, where the design fits the content, as opposed to a template where the content fits the design.” – Wix CEO Avishai Abrahami

Unlike cookie-cutter builders, the tool will create tailor-made sites, with unique designs and images created by Wix’s own AI technology, and text generated by ChatGPT.

According to the company, the site builder will be adept in building e-commerce, scheduling, ticketing, and food-ordering components automatically, and will let make personalized adjustments to the site after it has been created.

With Abrahami claiming the AI Site Generator to be the “next generation of website creation”, the feature is likely to make massive waves in the web builder community once released. But this isn’t the only smart AI-powered tool Wix is adding to its arsenal.

Read our guide to building a website using AI

AI Page Creator and Object Eraser Also Coming Soon

Businesses building sites from scratch won’t be the only ones to benefit from Wix’s pending updates. The software company is also set to release an AI Page and Selection Creator which will streamline the process of adding sections or pages to pre-existing sites.

Leveraging artificial intelligence in a similar way to the AI Site Generator, the tool will be able to create custom layouts, designs, and text in response to unique user-generated prompts.

The website builder will also be launching a new AI image manipulation tool called Object Eraser. This feature intends to improve the image editing process by offering advanced capabilities like extracting subjects from images.

The web builder hasn’t announced an official launch date for these features, so we’d recommend keeping up to date with its company blog for more information. However, you’ll still be able to benefit from a range of AI tools available now, such as its AI Text Creator, Alternative Layouts tool, and AI Image Creator.

Is Wix the Best Website Builder on the Market?

Wix’s constantly evolving offering and new powerful AI capabilities have secured its position as an industry leader.

After testing the product ourselves, we found it to be the best option out there for small businesses, thanks to its excellent user experience, advanced business functionality, and ample software integrations. Its shallow learning curve and affordable entry price made it the best builder we tested to get started on too.

However, despite its strengths, Wix lags behind Squarespace when it comes to design functionality – as the rival offers better quality templates and more customizable designs. For those building online stores, Shopify’s impressive ecommerce features take the edge over Wix, too.

You can read our Wix vs Shopify guide to learn more about how the web builders weigh up.

Since Elon Musk took the helm at Twitter HQ the social media company has lost almost half of its advertising revenue, according to a Tweet recently fired off by the tech entrepreneur on Sunday.

This advertiser exodus, combined with steadily rising debts which now amount to $13 billion, is keeping Twitter’s cash flow in the red, despite rash cost-cutting measures like multiple rounds of layoffs, office closures, and slashed employee paternal leave.

The company’s rocky financial situation is exacerbated by the recent release of ‘Twitter killer’ Threads — Meta’s text-based conversation app that has already surpassed 150 million downloads.

Twitter Fails to Meet Ad Revenue Targets

Nestled in between a series of controversial memes and hot takes, Elon Musk recently Tweeted that Twitter is still experiencing negative cash flow, due to a ‘~50% drop in advertising revenue’, and that they need to reach positive cash flow before having the ‘luxury of anything else.’

The Tweet also refers to Twitter’s heavy debt load, which has been growing incrementally since the billionaire took over the platform last October, and currently sits at around $13 billion.

We’re still negative cash flow, due to ~50% drop in advertising revenue plus heavy debt load. Need to reach positive cash flow before we have the luxury of anything else.

— Elon Musk (@elonmusk) July 15, 2023

Musk explained that “Twitter Spaces” – the company’s new live audio conversation platform – has yet to turn a profit and that the business wasn’t able to hit advertising revenue targets in June. However, he attests that “July is a bit more promising”.

These dismal financial projections fly in the face of Musk’s previous assertion that the company would be cashflow positive in June, and indicate a rocky start for new CEO Linda Yaccarino who has been implementing a series of fresh measures to try and attract advertisers back to the platform.

Twitter’s Financial Worries Deepen

Musk’s updates on Twitter’s grave financial situation come just three days after the Tesla CEO was struck by a $500 million class action lawsuit over unpaid severance payments.

The lawsuit, which was filed by Twitter’s former “Head of Total Rewards” Courtney McMillan, alleges that the company owes former workers around half a billion in severance pay, and that Musk was well aware of the established severance plan before making major reductions to the company’s headcount.

This case mirrors a similar lawsuit filed last month that takes aim at Twitter’s exec board for failing to pay workers bonuses that they were entitled to last year. Both cases accuse the company of not following through with payments promised to employees, and failing to communicate changes to affected workers.

If Twitter is forced to make these payouts, the damages will pile on the $13 billion of debt it amassed from its acquisition last year – making it even harder for the company to meet bill payments they’ve routinely been falling behind on.

Is Twitter Hanging on By a Thread?

Unfortunately for the blue bird, finances aren’t the only concern threatening the company’s survival in 2023.

Twitter is also facing rising competition from Meta-owned Threads, an insurgent text-based social media app that has already welcomed over 150 million users. Threads’ format and function are remarkably similar to Twitter’s – with both apps providing users with a way to share short messages, photos, and videos on public timelines.

Threads also has the additional benefit of being directly integrated with Instagram, allowing the photo-sharing app’s one billion users to sign up to the platform easily, and creating a verification process that’s much more legitimate and streamlined than Twitter.

This isn’t to say that Twitter is out of the race completely, however. The town-hall-style platform still offers a number of useful features that Threads lacks – including instant messaging and hashtags – and according to the troupe of Tweets Musk sent out last Sunday, the app’s usage is currently up 3.5% week on week.

Twitter has announced that it will begin sharing a portion of its ad revenues with verified creators who are signed up for Twitter Blue, the company’s premium subscription service.

To be eligible for the program, creators must have at least 5 million impressions on their posts in each of the last 3 months and have a Stripe payment account. Once they are eligible, creators can choose to opt in to the program and start earning a share of the revenue from ads that are displayed in their replies.

The amount of revenue that creators earn will depend on a number of factors, including the number of impressions their posts receive, the type of ads that are displayed, and the overall engagement of their audience. It’s the latest move by the social media giant to steady the ship and stop creators dumping the platform in favor of one of the many Twitter alternatives that now exist.

A Major Win for Twitter Creators

The launch of the ad revenue sharing program is a major win for creators on Twitter, who have been calling on the platform to find a way to help them monetize their content for years. Now, Twitter says that creators can expect to earn “a few cents” per impression with the company’s total first payout amounting to $5 million, cumulative from February.

As well as potentially helping to attract new creators, the move seems designed to help encourage existing ones to continue making content for the platform, as the fierce new Twitter vs Threads rivalry shows no signs of going away. 

Moreover, by showing support for its top creators and incentivizing them to make more and better content, Twitter will no doubt be hoping to spur on increased engagement across its entire user base.

How Much Do Twitter Creators Earn?

Good question. A Forbes report highlighted that one of the platform’s most-followed accounts, Internet hall of fame (@InternetH0F), enjoyed a whopping payout of $107,274. Elsewhere, writer Brian Krassenstein, who has about 750,000 followers, claims that Twitter paid him $24,305. On the lower end of the scale, a creator with about 230,000 followers claims to have earned $2,236.

Of course, there are some potential downsides to the program. For example, some creators may worry that the ads will clutter up their replies and make it harder for them to engage with their audience. Additionally, the program is only available to verified creators, which means that smaller creators may not be able to benefit from it.

However, the launch of the ad revenue sharing program is a generally being received as positive step for Twitter during a challenging time headlined by billionaire owner Elon Musk stepping aside as Twitter CEO, with former NBCUniversal ads boss Linda Yaccarino taking the reins.

Most Divisive Content Paying Dividends?

However, some Twitter creators have voiced concern that the most engaging tweets are those that divide opinion and attract extreme views. 

As Farzad Mesbahi tweeted: “The more haters you have in your replies the more money you’ll make on Twitter.” To which tech tycoon Elon Musk casually rebutted: “Poetic justice.”

Another concern is that as 5 million monthly impressions is such a high bar to reach, Twitter creators will be pushed to further sensationalize their content. “Will this turn into ‘click bait headlines’ but in the form of tweets?” asks Emmet Peppers, who holds a blue tick and describes themselves as an investor.

Only time will tell, but for big name Twitter creators with the necessary reach, today is undoubtedly a good day.

Microsoft-backed startup OpenAI will be probed by FTC, in investigation into claims the company misused personal data.

The Federal Trade Commission (FTC), the US’s top competition watchdog, is investigating growing suspicion that the Artificial Intelligence company, OpenAI, broke the law by scraping public data and publishing false and defamatory information.

In what is the biggest regulatory stumbling block since the launch of ChatGPT in late 2022, the 20-page letter demands OpenAI produce the records about how they address risks related to its AI models. The agency is investigating whether the company engaged in unfair or deceptive practices that resulted in “reputational harm” to consumers.

The FTC is reportedly concerned about the potential for ChatGPT to be used for harmful purposes, such as spreading misinformation or creating deepfakes. There have been multiple reports of the chatbot producing inaccurate and libelous statements about real individuals. 

An Effort to Clean Up the Wild West of the Internet?

According to a report by The Guardian, the FTC’s investigation into ChatGPT is part of a larger effort by the agency to regulate new and emerging technologies. The FTC has been particularly concerned about the potential for AI to be used for harmful purposes. In 2022, the FTC issued a report warning about the risks of AI-powered deep fakes.

The FTC’s investigation into ChatGPT is still in its early stages, but it highlights the growing concerns about the potential for AI to be used for harmful purposes.

The FTC has demanded to know whether OpenAI used an illegal process called “web scraping”  to obtain the information from the internet or purchased it from third parties. It also asks for the names of the websites that data has been taken from, as well as any steps taken to prevent personal information from being included in the training data.

The Washington Post was first to report the investigation. The FTC declined comment. OpenAI has also been contacted for comment.

What Does Investigation Mean for Consumers and Business?

The FTC’s investigation into ChatGPT is a reminder that AI is a powerful tool that can be used for both good and bad. Consumers and businesses alike need to be aware of the potential risks of AI and take steps to mitigate those risks.

Here are some tips for using AI safely and responsibly:

• Be aware of the potential for AI to be used for harmful purposes.
• Do your research before using any AI-powered product or service.
• Make sure that you understand the terms of service for any AI-powered product or service that you use.
• Be transparent about your use of AI with your customers and employees.
• Don’t share personal or sensitive data

What’s Next for ChatGPT?

It is too early to say what the future holds for ChatGPT. However, the FTC’s investigation is likely to have a significant impact on the development of the chatbot AI.

If the FTC finds that ChatGPT poses a significant risk to consumers, OpenAI may be required to make changes to the chatbot AI. These changes could include limiting the types of conversations that ChatGPT can have, or requiring users to provide more information about themselves before using the chatbot AI.

The FTC’s investigation is also likely to raise awareness of the potential risks of AI. This could lead to more businesses and consumers being cautious about using AI-powered products and services.

Apple has just released a new update to macOS Sonoma that brings its Password Manager to Chrome, Microsoft Edge and other browsers.

This update means that Mac users who prefer to use Google Chrome, Microsoft Edge, or another browser can now take advantage of Apple‘s powerful password manager.

We explain how to install the MacOS Sonoma Beta, and how to use Apple’s password manager from any browser.

What Does the Apple Password Manager do?

On July 12, tech giant Apple announced the first public beta of macOS Sonoma — the next major update for macOS that will be available to everyone this fall. The macOS Sonoma update comprises various new features, including the Apple Password Manager development.

In addition to the Chrome extension, Apple has also released an iCloud Passwords extension for Microsoft Edge. This extension is available from the Microsoft Edge Add-ons store.

The extension will automatically autofill passwords and one-time codes, and it will also generate strong passwords for new websites.

In addition to the convenience factor, Apple Password Manager also offers a number of security benefits. Apple’s password manager uses end-to-end encryption to protect your passwords, and it also includes features like two-factor authentication and password auditing. This makes it a very secure way to store your passwords.

The Apple Password Manager becoming available on third-party browsers makes it possible for Mac users to use Apple’s password manager no matter which browser they prefer. Plus, it makes it easier for users to switch browsers without having to worry about losing their passwords.

Read our ultimate guide to creating strong passwords

How to install the MacOS Sonoma Beta

If you are interested in using Apple Password Manager in public Beta, here are the steps you need to follow:

1. Go to Apple’s beta software portal.
2. If you haven’t installed a public beta before, sign up with your Apple ID.
3. Click macOS.
4. Download the macOS Public Beta Access Utility.
5. Run the .PKG file to sign up for beta updates.
6. Open Software Update and install the beta.

How to Use Apple Password Manager on Any Browser

1. Make sure that you are running macOS Sonoma or later.
2. Install the iCloud Passwords Chrome extension or the iCloud Passwords Microsoft Edge extension.
3. Sign in to your iCloud account in the extension.
4. Start using your Apple passwords in Chrome or Microsoft Edge.

Other New Features from the macOS Sonoma Update

The Apple Password Manager update was not the only new feature for Mac users to look forward to this fall: 

• This update includes interactive widgets for the desktop, making widgets more useful and more prominent. You can even use your iPhone widgets on your Mac’s desktop through a new Continuity feature.
• There is also a new game mode designed to limit distractions while playing games.
• Apple is also getting more serious about teleconferencing, with a new overlay feature that cuts out the speaker, playing them in front of a dynamic background. There are also new effects like fireworks.
• The new desktop browser is also getting improved wallpapers, including a shot of Sonoma. Safari’s getting a bunch of new features, including locking for private browsing and web apps that can be added to the system’s dock.
• There will be new functionality to keep your work and personal browsing profiles separate, along with increasing privacy safeguards

New survey data from project management software provider Wrike shows that more than three-quarters of knowledge workers are overwhelmed by the volume of apps they’re using and yearning for a “single source of truth” at work.

The vast array of software applications used by the average employee contributes to what Wrike calls the “Dark Matter” of work, the day-to-day tasks and processes that managers and supervisors often have little visibility over.

The proliferation of applications – known as  SaaS (Software as a Service) sprawl – leads to app fatigue, and in turn, creates the demand for centralized digital locations for planning, tracking and collaborating.

Employees Want A Single Source of Truth at Work

Wrike’s 2023 Efficiency Report sought responses 1005 business leaders and 2,002 knowledge workers across the UK and US, with a focus on individuals working in companies with more than 250 employees.

Out of those surveyed, 58% said they want to use fewer software apps to do their job, while 76% of workers report that a “single source of truth” would help reduce work-related stress.

51% said they’d feel happier if this single source of truth was leveraged in their workplace, while 52% said they’d feel more productive.

What’s more, the vast majority believe it would have a positive effect on cross-team collaboration (80%), and help achieve results (81%).

The lack of a single source of truth is also affecting visibility over work. According to Wrike’s data, workers believe the person in charge of their team only has visibility over 55% of the work that they do, leaving 45% unaccounted for.

Business leaders are under any false impressions about how much they’re privy too, either – they report, on average, that they only have visibility over 58% of the work that their team does.

Do We Use Too Much Software?

Business leaders said that, on average, their teams are using five to six (5.36) fewer applications than they were in 2022. This has, according to the business leaders involved in Wrike’s report, led to an 11% improvement in operational efficiency.

However, employees report that, on average, they’ve only been able to cut three to four (3.78) applications out of their working day over the past 12 months. This is probably due to the fact that “app integrations have not increased significantly” between 2022 and 2023, the report notes.

But precisely how many applications are being used by businesses?

Although recent data from large-scale studies on this topic is sparse, a 2021 report that analyzed data on 30,000 applications used by 91 companies found that “most departments use between 40-60 applications”.

Are Work OS Programs the Future?

The proliferation of software applications in workplaces is referred to as SaaS sprawl – in other words, a messy combination of unapproved and often poorly integrated applications, all fulfilling singular, one-dimensional functions.

“Knowledge workers are overwhelmed with the number of apps and communication tools they use at work in addition to increased workloads,” Wrike concludes in its report. “They are crying out for clear, streamlined, and consolidated processes.”

App fatigue is real and can have real downsides for businesses, along with increased stress for employees.

For instance, if employees feel like they’re already using an endless list of SaaS applications, then they might be reticent to consider a new one, even if there’s strong evidence that it will positively improve processes or generate ROI.

This is where project management apps like Wrike, as well as other productivity and work management platforms like monday.com, come into play. They’re specifically designed to function as all-in-one solutions, which is why so many companies are turning to them to simplify their day-to-day work processes.

Increasingly, these applications have begun to market themselves as “Work OS” programs – a relatively new concept, building on the popularity of productivity platforms. In the same way as your computer only needs one operating system, you only need one Work OS – and it might be the key to relieving work-related stress.

Despite a packed schedule of obnoxious tweeting, lawsuits, and organizing a cage match with Meta CEO Mark Zuckerberg, Elon Musk has found time to launch a new AI company called xAI.

Initially pinned as a generative AI startup for Twitter when it first hit the headlines back in April, the xAI website seems to suggest that the new startup will remain separate from the social media platform, as well as Musk’s collection of other technology companies.

The billionaire SpaceX chief certainly knows how to build an AI company, having co-founded ChatGPT owner OpenAI back in 2015, only to sell his stake three years due to a perceived conflict of interest with his electric vehicle company Tesla.

xAI: What We Know so far

xAI, as the company’s website states, has been set up to “understand the true nature of the Universe”. The one-page site includes very little information other than a list of current team members, all of whom are male.

According to Ars Technica, back in April, Musk secured a range of Graphic Processing Units (GPUs) from computer hardware behemoth Nvidia, necessary for building the large language models that power chatbots like Bard and ChatGPT.

The xAI website states that xAI will operate as a separate company from X Corp, “but will work closely with X (Twitter), Tesla, and other companies to make progress towards our mission”.

The site also says that xAI is “actively recruiting experienced engineers and researchers” in the San Francisco Bay and includes a sign-up form.

“Due to the number of applications we receive, we don’t have the capacity to respond to every individual application” the form reads. “If we would like to learn more about your skills and experience, we will be in touch within 3 weeks of receiving the application.”

Twitter Spaces Event Reveals More About xAI

Musk hosted a Twitter Spaces event this week, during which he discussed the new AI project in more detail.

In the event, Musk said he wanted to create a “maximally curious” artificial intelligence system, that would be “pro-humanity” in the sense that human existence is much more interesting than “non-humanity” – or in other words, human destruction.

The comments allude to the billionaire’s previously-expressed fears regarding the rapid pace of development of AI systems. Another Twitter Spaces event is set to be held on July 14th and will feature further discussion of the project.

Musk was one of several signatories who signed a letter demanding a research pause – which was published just two weeks after the billionaire registered the firm name “X.AI Corp” in Nevada, according to business records seen by the Financial Times.

Will xAI Rival OpenAI?

Bankrolled by Microsoft and following the release of ChatGPT, OpenAI has become the most well-known company developing artificial intelligence systems (specifically chatbots) on the planet.

Although the market has a number of interesting players – with DeepMind being the other clear frontrunner – but they rely heavily on funding Google, as do OpenAI from Microsoft. Google has also bankrolled Anthropic, an AI startup that just released Claude 2, another ChatGPT alternative.

Musk certainly has the capital, experience with AI companies – and now the hardware – to build a model capable of competing with the likes of ChatGPT and Bard.

Viewed through an optimistic lens, his public comments thus far suggest an ethical, reasoned approach to generative AI – two things that seem absent from his recent decision-making on social media and in the Threads vs Twitter war in particular.