Companies aren’t spending big on AI. Here’s why that cautious approach makes sense

Piggy banks in a line

Generative AI has captured the general public's attention, but that sense of excitement doesn't mean executives believe it's ready to be deployed in the business

Just one in ten technology leaders globally report having large-scale implementations of AI, according to Nash Squared's annual Digital Leadership Report, which is the world's largest and longest-running annual survey of technology chiefs.

What's more, the hype surrounding generative AI has done little to encourage further investment in artificial intelligence — Nash Squared reports the one in ten proportion who spend big on AI hasn't changed for five years.

Also: 4 ways to detect generative AI hype from reality

From the outside looking in, it seems the bark of AI is a lot louder than the bite. While everyone's talking about generative AI and machine learning, very few companies are investing in large-scale AI implementations.

However, Bev White, CEO of digital transformation and recruitment specialist Nash Squared, says in an interview with ZDNET that it's important to place these headline figures in context.

Yes, few businesses are spending big on AI right now, but lots of organizations are starting to investigate emerging technology.

"What we are seeing is actually quite an uptake," says White, who says interest in AI is at the research rather than the production stage.

Around half of companies (49%) are piloting or conducting a small-scale implementation of AI, and a third are exploring generative AI.

Also: AI at the edge: Fast times ahead for 5G and the Internet of Things

"And that's exactly what we saw when cloud started to really take off," says White, comparing the rise of AI to the initial move to the cloud over a decade ago.

"It was, 'let's dip our toe in the water, let's understand what all the implications are for policies, for data, for privacy, and for training,'" she says.

"Businesses were creating their own use cases by doing small but meaningful pilots. That's what happened last time, and I'm not surprised that's what's happening this time."

In fact, White says the hesitancy to spend big on AI makes a lot of sense for two key reasons.

First, cash is tight in many organizations due to heavy investment in IT during and immediately after the COVID-19 pandemic.

"Digital leaders are trying to balance the books — they're thinking 'what's going to give me the greatest return for investment right now,'" she says.

"Small, careful, well-planned pilots — while you're still doing some of the punchier digital transformation projects — will make a big difference to your organization."

Also: As developers learn the ins and outs of generative AI, non-developers will follow

Second, a lot of emerging technology — particularly generative AI — remains at a nascent stage of development. Each new iteration of a well-known large language model, such as OpenAI's ChatGPT, brings new developments and opportunities, but also risks, says White.

"You're accountable as a CIO or CTO of a big enterprise. You want to be sure about what you're doing with AI," she says. "There's such a big risk here that you need to think about your exposure — what do you need to protect the people that work for your business? What policies do you want to have?"

White talks about the importance of AI security and privacy, particularly when it comes to the potential for staff to train models using data that's owned by someone else, which could open the door to litigation.

"There's a big risk that people can cut and paste," she says. "I'm not saying generative AI isn't good. I'm really a fan. But I am saying that you've got to be very consciously aware of the sources of data and the decisions you make off the back of that information."

Also: Organizations are fighting for the ethical adoption of AI. Here's how you can help

Given these concerns about emerging technology, it might seem strange that Nash Squared reports that only 15% of digital leaders feel prepared for the demands of generative AI.

However, White says this lack of preparedness is understandable given the lack of clarity around both how to implement AI safely and securely today, and the potential for sudden changes in direction in the not-so-distant future.

"If you're accountable for the security, safety, and the reputation of using this technology inside your business, you'd better make sure you've thought everything through, and also that you take your board with you and educate them along the way," she says.

"A lot of chief executives know that they've got to have AI somewhere in their mix, because it's going to provide a competitive advantage, but they don't know where yet. It's a discovery phase, really."

White says the focus on exploration and investigation also helps to explain why just 21% of global organizations have an AI policy in place, and more than a third (36%) have no plans to create such a policy.

Also: The ethics of generative AI: How we can harness this powerful technology

"How many innovative projects do you know that started with people thinking about potential gates and failure points?" She says.

"Mostly you start with, 'Wow, where could I go with this?' And then you figure out what gates you need to close around you to keep your project and data safe and contained."

However, while professionals want to live a little when it comes to exploring the opportunities of AI, the research — which surveyed more than 2,000 digital leaders globally — suggests CIOs aren't oblivious to the need for strong governance in this fast-moving area.

In most cases, digital leaders are looking for regulations to help their organizations investigate AI safely and securely.

Yet they're also unconvinced that rules for AI from industry or government bodies will be effective.

While 88% of digital leaders believe heavier AI regulation is essential, as many as 61% say tighter regulation won't solve all the issues and risks that come with emerging technology.

Also: Worried about AI gobbling up your job? Start doing these 3 things now

"You'll always need a straw man to push back at. And it's good to have guidance from industry bodies and from governments that you can push your own thinking up against," says White. "But you won't necessarily like it. If it's carried through and put into law, then suddenly you've got to adhere to it and find a way of keeping within those guidelines. So, regulation can be a blessing and a curse."

Even if regulations are slow to emerge in the fast-moving area of AI, White says that's no excuse for complacency for the companies who are looking to investigate the technology.

Digital leaders, particularly security chiefs, should be thinking right now about their own guardrails for the use of AI within the enterprise.

And that's something that's happening within her own organization.

Also: Your AI experiments will fail if you don't focus on this special ingredient

"Our CISO has been thinking about generative AI and how it can be a real gift to cyber criminals. It can open doors innocently to important, big chunks of data. It could mean access to your secret sauce. You have to weigh up the risks alongside the benefits," she says.

With that balance in mind, White issues a word of warning to professionals — get ready for some high-profile AI incidents.

Just as a cybersecurity incident that affects a few people can help to show the risks to many others, AI incidents — such as data leaks, hallucinations, and litigations — will cause senior professionals to pause and reflect as they explore emerging technology.

"As leaders, we need to be concerned, but we also need to be curious. We need to lean in and get involved, so that we can see the opportunities that are out there," she says.

Artificial Intelligence

Google’s Strategic Expansion in AI: A $2 Billion Bet on Anthropic

In a move that underscores the tech giant's deepening commitment to artificial intelligence (AI), Google has recently announced a significant investment in Anthropic. This $2 billion infusion not only strengthens Google's foothold in the rapidly evolving AI landscape but also signals a profound shift in the industry's dynamics.

Anthropic, a burgeoning rival to OpenAI, the creators of the widely acclaimed ChatGPT, has become a focal point in the race to dominate the next generation of AI technologies. Google's substantial investment, which follows a previous allocation of $550 million earlier in 2023, is more than just a financial endorsement. It represents a strategic alignment with Anthropic's vision and technological aspirations.

This investment is particularly noteworthy in the context of the broader AI industry, which is witnessing unprecedented growth and competition. With tech behemoths like Amazon and Microsoft also placing hefty bets on AI startups, the landscape is rapidly becoming a battleground for innovation, talent, and market dominance. Google's latest move with Anthropic is not just about backing an AI startup; it's about shaping the future of AI and securing a leading position in an increasingly competitive field.

Google's Growing Investment in Anthropic

Google's foray into the world of advanced artificial intelligence through Anthropic started with an initial investment of $500 million. This substantial amount laid the groundwork for a deeper financial commitment, which has now crescendoed to a staggering $2 billion.

Alongside direct investments, Google Cloud has entered into a multiyear partnership with Anthropic, valued at over $3 billion. This alliance is not just a financial transaction but a strategic collaboration that could leverage Google Cloud's robust infrastructure to bolster Anthropic's AI development. This deal represents a symbiotic relationship, promising to accelerate Anthropic's AI innovations while enhancing Google Cloud's position as a preferred platform for cutting-edge AI research and deployment.

In a competitive landscape, it's worth noting that Google is not the only tech titan betting big on Anthropic. Amazon has also made a significant move by investing a colossal $4 billion into the AI startup. This investment by Amazon, known for its strategic forays into future technologies, further validates Anthropic's potential and places it at the center of a high-stakes tech rivalry.

The OpenAI-Microsoft Parallel

This escalating investment scenario is reminiscent of the partnership between OpenAI and Microsoft, which has seen Microsoft pour over $13 billion into OpenAI since 2019. The relationship between OpenAI and Microsoft, particularly in the wake of the sensational success of ChatGPT, has set a precedent in the industry. Google's increasing involvement with Anthropic can be seen as a direct response to this, positioning the tech giant as a formidable contender in the race to lead the AI revolution.

Google's deepening financial and strategic involvement with Anthropic, juxtaposed with similar moves by Amazon and Microsoft's alliance with OpenAI, is reshaping the AI industry landscape. It's a clear indicator that the battle for AI supremacy is intensifying, with major players making significant investments to secure their positions at the forefront of this technological evolution.

Big Tech Companies Are Not Even Trying to Build Better AI

When dealing with high-tech giants the notion of voluntary commitment to building AI responsibly is a joke. Google and Amazon’s union-busting attempts, Meta’s Cambridge Analytica scandal, and Microsoft and OpenAI’s copyrights violations are only the tip of the iceberg showing voluntary commitments should not be expected to yield better results.

These issues with AI models keep several researchers awake at night. Swedish philosopher Nick Bostrom is one of the ones who ponder on, “How do we ensure that highly cognitively capable systems — and eventually superintelligent AIs — do what their designers intend for them to do?”. Bostrom has delved deeper into the unsolved technical problem in his book ‘Super Intelligence’ to draw more attention to the subject.

An infamous example of misalignment: When an algorithm by Google was trained on a data set of millions of labelled images, it was able to sort photos into categories as fine-grained as “Graduation” — yet classified people of colour as “Gorillas.” But why do these issues continue to persist despite time and again companies’ “efforts” in building better algorithms and models?

Irene Solaiman, the policy director at Hugging Face believes that a part of the trouble of alignment issues is the lack of consensus on what constitutes the field of value alignment. “Generally a definition is following developer intent, but so many peoples affected are not reflected in developer teams,” she added.

Solaiman who was a part of the GPT-2 and GPT-3 teams is a strong proponent of inclusive value alignment that recognizes asymmetries in who is affected. For example, she said, “I would consider harmful stereotyping and biases part of alignment work. The number of researchers differs based on how we consider technical and social scientists and how close they are to model development and release.”

The State of Aligned AI

The latest State of AI report also points out the lack of researchers in AI actively working on preventing these models from being too misaligned. Cumulatively, there is a tiny group — across seven lead organisations of less than a hundred researchers, an extremely tiny fraction of the AI research community worldwide.

As per the report, Google DeepMind has the largest and most established AI alignment team of 40 members led by co-founder Shane Legg. In comparison, OpenAI has a team of 11 members, and its rival startup Anthropic has 10.

OpenAI recently formed a team called “Preparedness” to assess, evaluate and probe AI models to protect against what it describes as “catastrophic risks.” A few months ago, it also announced a super alignment project. The idea boiled down to, ‘we observe that advanced AI is dangerous, so we build an advanced AI to fix this problem’. Professor Olle Haggstrom, who was one of the signatories to call for a 6-month pause on building more advanced AI than GPT-4 called it ‘a dangerous leap out in the dark.’

“Tomorrow’s AI poses risks today,” he emphasised, citing insights from a Nature article. In the ongoing AI discussions, a regrettable divide persists between those who focus on immediate AI risks and those who consider longer-term dangers. However, with growing awareness that a maximally dangerous breakthrough might not be decades away, these two factions should join forces, Haggstrom advises. “ Neither of these groups of AI ethicists are getting what they want,” he said while noting the lack of focus on building aligned AI.

Just a Label

While DeepMind has advocated for safer, aligned models, since day one. Its counterpart Google has other plans. The tech titan recently pledged $20 million for a responsible AI fund. The search giant made $60 billion in profit in 2022 which means 0.0003% of their profit is directed towards building better models.

Google has been carrying around the ‘bold and responsible’ label since its executives have not shut up about generative AI since 2023 began. The new mantra has been repeated over and over in all the conferences held from Mountain View to Bangalore.

But it’s not the only one. For the majority of big tech companies being “responsible” has become a tactic to stay books of the media and communities. Behind the back, their shenanigans continues showing that organisations akin to Google and OpenAI do not genuinely care about ethics over profitability.

The post Big Tech Companies Are Not Even Trying to Build Better AI appeared first on Analytics India Magazine.

Why Google and OpenAI Should Consider Open Sourcing Old Models

When Meta entered the generative AI space anxious after the launch of OpenAI’s GPT-3.5-based ChatGPT, they were actually clueless about how to even register their presence in the AI space. OpenAI was the cynosure of all eyes in this space.

However, an unexpected incident occurred at Meta when their LLaMA model, created to assist researchers, was leaked on 4chan just a week after its announcement in March. The incident, which initially threatened to spell ‘game over’ for Meta, transformed the AI space forever and the company became the biggest player in the open source AI community.

In May, a memo leaked from a Google researcher who was sceptical about the future of Google and OpenAI in the AI field as both the companies had “no moat” in the industry. The researcher’s concern was widely discussed in the ecosystem and a few months later, after the wide success of LLaMA and Llama 2, the researcher’s scepticism seems to be coming true.

Don't understand why companies like Open AI and Google won't open-source their last gen LLMs and models.
Shouldn't Open AI open-source GPT-3.0 and Google, early versions of PaLM, at least for research purposes?
They would get so much love and affection from the tech and…

— Bindu Reddy (@bindureddy) October 25, 2023

In March, Google announced giving access to developers to its large language model PaLM via API. Along with the API, the tech giant also announced a new app called MakerSuite. “With MakerSuite, you’ll be able to iterate on prompts, augment your dataset with synthetic data, and easily tune custom models,” said the company in a press release.

The announcements sound big, but not enough from the perspective of open source. Though the company is still doubling down on AI with its recently launched PaLM 2, a powerful language model with improved multilingual, reasoning, and coding abilities, it should still open source the previous PaLM model launched in April, last year.

Imagining Open Source PaLM World

The first and foremost impact of an open-source PaLM will be on research and development. Open sourcing PaLM would make it available to a wider range of researchers and developers, who could use it to experiment with new AI techniques and develop new AI applications. This could lead to a rapid acceleration in AI research and development.

Open sourcing PaLM would encourage collaboration between researchers and developers from different organisations. This would help create a more vibrant and productive AI ecosystem. Besides, it will provide a more equitable access to AI. It would make PaLM more accessible to researchers and developers all over the world, regardless of their financial resources, thus reducing the digital divide.

The biggest issue with any AI is transparency and accountability. With PaLM open source, researchers and developers would be able to inspect and audit the model’s code, which would help make AI more transparent and accountable. This would be important for building trust in AI and ensuring that it is used responsibly.

All these improvements will lead to the development of new medical diagnostic tools and new AI-powered tools for predicting patient outcomes. Education is another sector where the model could be used to develop new AI-powered tools that tailor learning to each student’s individual needs.

PaLM could be used to develop new AI-powered tools for solving complex problems in areas such as climate change, energy, and transportation. This could help the community to create a more sustainable and equitable future.

OpenAI Should Pay Heed

Like Google, OpenAI too does not have any moat against other LLMs and the users are continuously declining. In June and July, the number of active users on ChatGPT decreased by nearly 10% each month. In August, it logged a 3% drop in global users.

OpenAI now has GPT-4 to boast and is expected to release the next model in coming months. It’s time the company open-sourced its GPT-3 to catch up to the expanding open source segment.

GPT-3, with a whopping 175 billion parameters, can transform the open source game completely.

What sets GPT-3 apart is its few-shot and zero-shot learning capabilities. This means that the model can perform tasks with minimal training examples or even without prior training on a specific task, making it adaptable and quick to take on new challenges.

GPT-3’s applications are diverse. It can be fine-tuned for various purposes, from chatbots and virtual assistants to content generation and even code generation. The model’s text generation is of high quality, often producing output that is indistinguishable from human-authored content, which makes it highly valuable for generating text and responding to user queries.

Furthermore, GPT-3’s impact extends beyond its practical applications. It has spurred significant research and development in the field of natural language processing, pushing the boundaries of what can be achieved in AI-driven text generation.

With Great Power Comes…

There is a lot of discussion about AI safety and OpenAI has raised its voice against open source, which it sees as a big threat. It loves to paint an eerie picture of uncontrolled AI. Both these companies fear that these powerful AI models would be used for malicious purposes, such as generating fake news or propaganda.

Besides, open sourcing would make it easier for competitors to replicate their AI capabilities, at times giving them an edge over the creator themselves. While open sourcing LLMs is not without its challenges, its existence could drive innovation in AI and natural language processing, shaping the future of this field like LLaMA and Llama 2.

The post Why Google and OpenAI Should Consider Open Sourcing Old Models appeared first on Analytics India Magazine.

Google Map’s new AI-powered Immersive View gives you more route detail than ever

googlemapsimmersive

Earlier this year, Google announced a new "Immersive View" feature for Google Maps. Using the power of AI, the tool takes flat pictures and constructs 3D images of landmarks, restaurants, and certain buildings. This gives users a better idea of what to expect at that location.

And now that view is rolling out to navigation and should be available to users soon.

Also: Google expands bug bounty program to include rewards for AI attack scenarios

Starting this week, new 3D views of 15 cities will be available for driving, walking, and cycling, letting users prepare with a new look at the turn-by-turn guidance. Instead of the traditional satellite view or Google Street View, there's now a hybrid of the two in the form of a 3D image that shows the entire route.

The included cities are Amsterdam, Barcelona, Dublin, Florence, Las Vegas, London, Los Angeles, Miami, New York, Paris, San Francisco, San Jose, Seattle, Tokyo, and Venice.

If you want to plan ahead, there's a slider to see the route at different times of day and with the projected weather — all powered by AI. The feature is somewhat similar to Google Street View, but since it's based on a combination of several different types of photos, the new view is more recent and more customizable.

Google is also adding some features to help Maps function more like a general search. Now, instead of searching for a specific place or address on Maps, users can try more general search queries like "artsy things nearby" or "coffee shops with latte art nearby." The former will bring up local attractions categorized on a scrollable carousel and the latter actually searches user-uploaded photos to find that type of thing and shows where those photos were taken as pins on a map.

Also: Google's new AI-powered tool helps users learn English right in Search

To use the feature, head to the Google Maps app on Android or iOS, pull up a location in one of the available cities, and click the "Immersive View" button to launch.

In addition, "Lens in Maps" augmented reality search is available in more than 50 new cities. With this feature (formerly available in a more limited format and called "Search With Live View"), you can quickly get a look at your surroundings by tapping the Lens icon in the search bar and lifting your phone to look around. If you're in a supported location, you'll see ATMs, bus or train stations, stores, restaurants, coffee shops, and more.

Google

ChatGPT seems to be confused about when its knowledge ends

ChatGPT login

Despite ChatGPT's many incredible capabilities, it has one big Achilles heel — a lack of information on current events. However, it seems like OpenAI might be quietly working on a solution.

When OpenAI first unveiled ChatGPT nearly a year ago, the AI chatbot only had knowledge of information that had occurred before September 2021 since the data it was trained on only covered that scope of time.

Also: The best AI chatbots

Recently, however, some users have been taking to X (formerly Twitter) to share that they noticed an expansion in the time frame of knowledge that the chatbot possesses.

In the example above, the user asked ChatGPT Plus with GPT-4 what its knowledge cutoff is, and it responded with "September 2023," which makes ChatGPT's scope of knowledge extremely recent.

Other ChatGPT Plus users in the thread got the same response from the AI chatbot regarding its scope of knowledge. However, when asked about current events that occurred within the period of time that it claims to be aware of, ChatGPT didn't seem to have the answers.

ZDNET decided to put it to the test and asked both ChatGPT Plus with GPT-4 and standard ChatGPT with GPT-3.5 what its scope of knowledge is. In both cases, ChatGPT responded with January 2022.

Although it's not as recent as what the other users were getting, if functional, a knowledge scope expansion to January 2022 would still be a significant advancement for the chatbot from its initial cutoff of September 2021.

Also: GPT-3.5 vs GPT-4: Is ChatGPT Plus worth its subscription fee?

Similar to the experience described by the X users, despite ChatGPT claiming to have knowledge of information from January 2022, it wasn't able to answer questions about events that happened in December 2021.

When asked when the first Omnicron death happened and who won the 70th Miss Universe pageant, ChatGPT said it didn't have access to that information since it occurred after its January 2022 cutoff date — which is false — and to check a website for the most recent information.

OpenAI's ChatGPT FAQ page, which was last updated last week, states that ChatGPT, "has limited knowledge of world and events after 2021 and may also occasionally produce harmful instructions or biased content."

Also: 8 ways to reduce ChatGPT hallucinations

The reason ChatGPT is claiming to have knowledge that it doesn't have isn't clear. ZDNET reached out to OpenAI for comment.

However, what it does point out is the need to verify the information you are getting from ChatGPT since, like any other generative AI model, it is prone to hallucinations.

Artificial Intelligence

The AI I want to see in the world: 5 ways it could manage my Gmail inbox for me

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OK, Google: Can we talk about what I really want from an AI assistant?

Many of us have become familiar with generative AI's ability to write some text for us in response to a prompt. It's becoming so common, in fact, that many tech companies are racing to add that capability to their products, often with an upsell fee.

Also: AI is a lot like streaming. The add-ons add up fast

A lot of value can be derived from text generation. It can reduce time, help focus thoughts, help folks who don't write very well produce professional-looking correspondence, and so much more. ZDNET has been covering this aspect of generative AI all year. Gmail's Help Me Write feature can write a message based on a prompt, formalize it, extend it, shorten it, or try a completely new draft.

But I want something more. I want Google's AI to help me manage my email.

I have roughly half a million email messages in my Gmail email store, going back to the beginning of Gmail. I get hundreds of new messages a day. Gmail's anti-spam filter does a fairly good job of keeping the most egregious spam from landing in my inbox, and I have a carefully curated library of filters that helps me manage the rest.

Also: How to write better ChatGPT prompts for the best generative AI results

But it's still a daily slog, and it could be so much better. Filters were introduced in 2004, shortly after Gmail launched. They haven't really been updated since. We're using a nearly 20-year-old technology to manage the daily onslaught of message traffic.

I spend a lot of my day managing email. Sure, a lot of it is stuff I can't delegate, like careful correspondence between team members and project partners. But I put in at least 30 minutes a day just managing the flow.

Even with that level of time investment, my five main email categories — Primary, Promotions, Social, Updates, and Forums — contain 41,330 messages. My approach is to let those messages that flow off the main page just accumulate, with the assumption that if something is truly important, I'm either tracking it elsewhere, or the person who sent the message will reach out to me again.

I want an AI assistant I can depend on like an assistant

What I want is an AI assistant I can train to manage all my existing messages and all the messages that come in each day. Filters help, but they rot over time as email addresses and message headers change, and they don't update to reflect new messages or topics being managed.

I want an AI assistant that I can collaborate with, that can help me manage this flow.

I get a tremendous number of press releases and pitches of all sorts from folks hoping I'll cover their products here on ZDNET. That's in addition to the regular promotions, newsletters, mailings, and other flow non-press folks get.

Also: 7 advanced ChatGPT prompt-writing tips you need to know

I've attempted over the years to set up filters for all the main PR firms, moving their messages out of my Primary category and into my Promotions category. I also drag and drop every press release from Primary into Promotions, which also has helped me train Gmail. But now I have 26,722 messages in Promotions, including the 114 that came in during the last 24 hours.

To show you how this might work, I'm going to run through five examples. Keep in mind that the prompts shown here are speculative. This feature does not exist in Gmail. Frankly, I'm hoping the Gmail team takes this article as inspiration and adds some of these features. If not, it might serve as inspiration to other email vendors and those incorporating AI to think outside the generative box and consider how large language models can do more than write basic prose for us.

With that, let's dive into the examples.

1. Sort press releases into a Gmail label

I'd like to give it some instructions. For example:

From now on, whenever a new press release comes in, remove it from the inbox and assign it to the Press Releases label.

Ideally, what it would do is just that: find messages that are press releases and file them. This is already better than filters, because sometimes I get correspondence from PR firms that are not new press releases. They're helping me with a story I'm researching, or I found a release interesting and I'm following up. I don't want those messages in my Press Releases folder. But if I filter by, say, email address, that granularity isn't possible.

Also: Less typing, fewer mistakes: How Gmail Snippets can save you time and effort

I also tried filtering on common press release terms, like "for immediate release" or "embargo", but the filter was never universal enough to succeed. AI is smarter than that, and should be able to separate press releases from correspondence, and actually know what email messages are press pitches.

2. Unsubscribe from unread newsletters

Here's another. I get a lot of newsletters, including a few I even subscribed to. I read about five or six of them regularly. But most of them just pile up, unread, because whatever interest I had back in the day got supplanted by the next thing I had to pay attention to.

So wouldn't it be nice if I could tell Gmail:

From now on, unsubscribe to any newsletter I haven't opened for 60 days.

The AI, of course, would have to know what a newsletter is. Waiting 60 days gives me time to possibly open something. But all the rest could be removed — and that list would change as time went on.

3. Keep the most recent promotional emails

I get a lot of promotional mailers from tool companies like Harbor Freight and Rockler. I also get a lot from photo and video companies. I value these sale flyers because, once in a while, they have something I want. But they accumulate. Right now, I have 962 items from Harbor Freight and 242 from Rockler, not to mention those from B&H and Adorama.

Also: Six skills you need to become an AI prompt engineer

Again, filtering won't help. Nor will search. That's because — in addition to the promotional information — I have purchase receipts and correspondence. I don't want that stuff deleted.

With that in mind, wouldn't this be nice?

From now on, whenever I get a promotional mailing from any of the tool or photo companies, keep the newest and delete all the rest. Be careful to differentiate between promotional mailings and purchase receipts. Keep all my purchase receipts forever.

4. Highlight emails from my team colleagues

I have a filter that looks for any email coming from zdnet.com and gives it a red ZDNET label. That way, emails from my ZDNET editors stand out in my inbox. But that filter doesn't capture email messages coming from the rest of the ZDNET contributors, many of whom do not have zdnet.com email addresses.

So, I'd love to be able to give this prompt to my AI assistant:

From now on, assign the ZDNET label to every email coming from someone with a zdnet.com email address. Every Monday, look at the Meet The Team page and get the names of everyone listed. Assign the ZDNET label to every email coming from everyone on that list as well. Make sure these messages are always assigned to the Primary category and marked as Important.

5. Summarize the news I might care about

So far, we've looked at sorting and flagging. But we could put the AI's generative skills to use as well. Wouldn't it be nice to get a summary of the five or ten press releases that might be relevant to the work I'm doing?

As is helpful any time you're creating a prompt, it's good to map out what you want the AI to do. So let's do that here:

  • Do a scan every weekday at 8am.
  • Scan press releases and pitches that have come in since the last scan. This will help cover releases that come in over the weekend. Had we said "in the last day," Monday scans would miss things that come in Friday afternoon and Saturday.
  • Look for pitches on a series of topics. For the purpose of this example, let's say those topics are AI, software development, and supply chain.
  • Select the five best pitches on each topic. We would define "best" as from PR firms we've previously corresponded with, are complete (including a description of the vendor and a URL link), and include at least one executive statement. There may be ways to refine this criteria, but this is a good starting point.
  • Define how to present the information. I'd like the company name, a single sentence summarizing the press release, and a link to the original email message.

Also: How to send password-protected emails in Gmail

OK, let's try to turn that into a prompt.

From now on, every weekday at 8am, do the following:

Scan press releases and pitches that have come in since the last scan. If this is your first scan, scan press releases and pitches that have arrived in the last 24 hours.

Of the scanned press releases, choose five each that are primarily about AI, software development, and supply chain. Only choose releases that include a description of the vendor, a URL, and at least one executive statement. If there are more than five releases on each subject, choose the five releases that contain the most information.

Create an email message. Set the subject as "Press release summary for " and then follow it with the date. Set the contents to be three bulleted lists, one for each of the topics. Prior to each list, display the topic with H2 formatting. Each bullet will correspond to one selected press release. List the company name, then provide a one-sentence summary of the press release, and then provide a link to the original email message.

Send that email message to me.

This would take some tuning, but it's a powerful idea. Instead of my having to sift through all that email, the AI could prepare a daily dossier on the most recent news.

Guardrails and caveats

Some interesting challenges come to mind when creating a feature set like this, especially given how prone the AI tools are to error. If this capability were to be deployed, it would need a testing interface and an editing interface.

The testing interface could be fairly simple. Gmail could simulate and then display the various moves the AI would make to email messages, without actually doing anything. I'd recommend this be in the form of snapshots in time, so users could let the simulator run for a few days, see what would have happened if the AI were unleashed, and then make changes to the prompts.

Also: The moment I realized ChatGPT Plus was a game-changer for my business

That brings us to the second guardrail feature: a prompt editor. Users would need to be able to go back and edit the prompts, especially when they're persistent ones intended as "from now on." This could be a fairly simple interface, but refinement would be essential with such open-ended instructions and the scope of the possible changes.

I know I would very much appreciate an AI assistant like this, but it's important for both users and developers to proceed with care. Google has a track record of releasing innovative features as beta releases, and I'd expect nothing different here. As a user, it would be wise to adopt slowly, especially if there's no testing interface. Try very simple prompts, wait and see how things work for a week or so, and then add to the instruction set.

Also: Google's new tools help users verify the authenticity of images online faster

Would you use this if it were available? Are there other capabilities you'd want? What would you ask your assistant to do? Let us know in the comments below.

You can follow my day-to-day project updates on social media. Be sure to subscribe to my weekly update newsletter on Substack, and follow me on Twitter at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, and on YouTube at YouTube.com/DavidGewirtzTV.

Artificial Intelligence

OpenAI assembles team of experts to fight ‘catastrophic’ AI risks — including nuclear war

Globe representing AI use

As AI continues to revolutionize how we interact with technology, there's no denying that it's going to have an incredible impact on our future. There's also no denying that AI has some pretty serious risks if left unchecked.

Enter a new team of experts assembled by OpenAI.

Also: Google expands bug bounty program to include rewards for AI attack scenarios

Designed to help fight what it calls "catastrophic" risks, the team of experts at OpenAI — called Preparedness — plans to evaluate current and future projected AI models for several risk factors. Those include individualized persuasion (or matching the content of a message to what the recipient wants to hear), overall cybersecurity, autonomous replication and adaptation (or, an AI changing itself on its own), and even extinction-level threats like chemical, biological, radiological, and nuclear attacks.

If AI starting a nuclear war seems a little far-fetched, remember that it was just earlier this year that a group of top AI researchers, engineers, and CEOs including Google DeepMind CEO Demis Hassabis ominously warned, "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war."

How could AI possibly cause a nuclear war? Computers are ever-present in determining when, where, and how military strikes happen these days, and AI will most certainly be involved. But, AI is prone to hallucinations and doesn't necessarily hold the same philosophies a human might have. In short, AI might decide it's time for a nuclear strike when it's not.

Also: Organizations are fighting for the ethical adoption of AI. Here's how you can help

"We believe that frontier AI models, which will exceed the capabilities currently present in the most advanced existing models," a statement from OpenAI read, "have the potential to benefit all of humanity. But they also pose increasingly severe risks."

To help keep AI in check, OpenAI says, the team will focus on three main questions:

  • When purposefully misused, just how dangerous are the frontier AI systems we have today and those coming in the future?
  • If frontier AI model weights were stolen, what exactly could a malicious actor do?
  • How can a framework that monitors, evaluates, predicts, and protects against the dangerous capabilities of frontier AI systems be built?

Heading this team is Aleksander Madry, Director of the MIT Center for Deployable Machine Learning and a faculty co-lead of the MIT AI Policy Forum.

Also: The ethics of generative AI: How we can harness this powerful technology

To expand its research, OpenAI also launched what it's calling the "AI Preparedness Challenge" for catastrophic misuse prevention. The company is offering up to $25,000 in API credits to up to 10 top submissions that publish probable, but potentially catastrophic misuse of OpenAI.

Few APAC firms will benefit from AI due to doubt and data management

Globe with pins on it

Artificial intelligence (AI) will continue to garner business interest over the next year, but few organizations in Asia-Pacific will be able to reap its full benefits due to their aversion to risk and subpar data management capabilities.

Just 30% have the IT practices that are needed to harness the benefits of AI, including greater operational resilience, richer customer experience, and business model innovation. Others will be held back by a risk-averse culture and inadequate data management capabilities, according to Forrester's 2024 predictions.

Also: AI is transforming organizations everywhere. How these 6 companies are leading the way

"While many Asia-Pacific firms see generative AI as a tool to boost efficiency, many will encounter obstacles in the form of an overly cautious work culture and gaps in data management capabilities," Frederic Giron, Forrester's vice president and senior research director, wrote in a blog post.

"Companies with more advanced IT practices, which make up about 30% of firms in the region, seem better prepared to make the most of the tech and are focused on using generative AI to evolve their business models," Giron noted. "But it's essential to note that this evolution will be more of a gradual multiyear process than an overnight success story in Asia-Pacific."

He pointed to two other key forecasts for the region, where a fourth of multinational corporations will adopt customer trust as a priority. Just 5%, though, are expected to have established tangible metrics to measure and uphold this trust.

While fundamental to business, public trust currently is on a decline, he added.

Forrester noted that companies in the region will struggle to operationalize customer trust, with few taking such efforts seriously and working to integrate it into their corporate culture.

By the end of 2024, the research firm projects 25% of large enterprises in Asia-Pacific will advocate their commitment to earning customer trust.

"The real challenge, however, lies in putting these words into measurable actions," Giron said.

Also: If AI is the future of your business, should the CIO be the one in control?

In spite of this, customer experience is expected to improve with the adoption of generative AI in the backend, with customer service agents gaining the ability to respond to questions faster and better. Issues will be resolved on first contact, resulting in customers feeling heard, according to Forrester.

The firm predicted that agencies will invest in custom AI applications on behalf of organizations, with the top 10 advertising agencies collectively spending $50 million next year in partnerships to build such applications. These AI tools will allow their clients to scale customized marketing campaigns, Forrester said.

Business AI initiatives also will lead to a 50% improvement in productivity and problem-solving, where generative AI is expected to enhance productivity across all IT roles, including developers. Investments in the technology across the organization will further boost employees' problem-solving time by up to 50%.

Enterprise adoption of prompt engineering services, however, will be limited. Amid cloud hyperscalers' efforts to introduce either previews or general availability of such services, 80% of businesses will add prompt engineering talent internally to drive model grounding and value.

Also: The best AI chatbots

With regulators working on potential generative AI policies, Forrester recommends organizations identify applications that may add to their risk exposure and invest in third-party risk management tools.

"In 2024, Asia-Pacific is bracing itself for a year of exploration and potential growth, with generative AI at the center of it all," said Giron. "The promise and potential of generative AI, combined with a new wave of technological innovations, will inspire more Asia-Pacific tech and business leaders to follow in the footsteps of early trailblazers and fuse the power of AI with their transformation efforts to drive business outcomes."

Artificial Intelligence

This new camera embeds authenticity details in photos, but it doesn’t come cheap

leica-camera-back-w-photo

With the rise of AI-generated images, distinguishing between fabricated and authentic images is increasingly difficult. The camera manufacturer Leica is attempting to combat that issue with the release of its latest camera, the Leica M11-P.

On Thursday, Leica dropped the Leica M11-P, the world's first camera to have Content Credentials built in, which enables a picture to have detailed metadata included at the point of capture and essentially serves as a verification stamp for the image.

Also: The best AI art generators: DALL-E 2 and fun alternatives to try

The metadata includes details such as the camera make and model, who captured the image, and when and how the image was captured, as seen by the photo below.

Each image will have its own digital signature that can be easily used to verify the authenticity of the images on the Content Credentials site or the Leica FOTOS app, according to the release.

Also: How to become a content creator: Everything you need

"The Leica M11-P launch will advance the CAI's goal of empowering photographers everywhere to attach Content Credentials to their images at the point of capture, creating a chain of authenticity from camera to cloud and enabling photographers to maintain a degree of control over their art, story and context," said Santiago Lyon, head of advocacy and education at the Content Authenticity Initiative (CAI).

If a user does not want to participate and would rather use the camera like they would with any other device, the Content Credentials feature works on an opt-in basis.

The secure metadata meets the CoaliIon for Content Provenance and Authenticity (C2PA) standard, a Joint Development Foundation that combines the Adobe-led CAI and Project Origin, a Microsoft- and BBC-led initiative focused on tackling misinformation in digital news.

According to the C2PA site, the organization is dedicated to building "an end-to-end open technical standard to provide publishers, creators, and consumers with opt-in, flexible ways to understand the authenticity and provenance of different types of media."

Also: How to level up your iPhone photo skills

In addition to the Content Credentials feature, the camera comes with other specs that make it a compelling purchase, including a 60MP BSI CMOS sensor, Triple Resolution Technology, a Maestro-III processor, and 256GB of internal memory.

The Leica M11-P will retail for €8,950, roughly $9,461, and will be available globally at all Leica Stores online and authorized dealers, starting today.

Artificial Intelligence