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Microsoft Announces New Maia 100 and Cobalt 100 Chips

Microsoft will make two new chips available next year, the tech giant announced at the Microsoft Ignite conference on Nov. 15. The Microsoft Azure Maia 100 is designed for AI workloads, and the Microsoft Cobalt 100 CPU is designed for general compute workloads on Microsoft cloud.

Jump to:

  • Custom chips built to in-house specifications
  • New chips were designed for cloud workloads
  • Microsoft’s relationship to its silicon competitors

Custom chips built to in-house specifications

Both the Maia 100 and Cobalt 100 chips are built in-house by Microsoft, which the tech giant says enables “everything from silicon choices, software and servers to racks and cooling systems” to be tailored to the customer workloads Microsoft predicts it will see, according to a press release.

The Microsoft Azure Maia 100 AI Accelerator is optimized for AI tasks and generative AI (Figure A). Microsoft shared their designs for the Maia 100 with OpenAI to ensure the Maia 100 would be optimized for large language workloads.

Figure A:

Image of Microsoft Maia 100 chip.
The Azure Maia 100 AI Accelerator. Image: Microsoft

The Microsoft Cobalt 100 CPU is an Arm-based processor designed for Microsoft Cloud (Figure B).

Figure B:

Microsoft Cobalt Datacenters.
Servers in a data center in Quincy, Washington, were the first use case for the Cobalt 100 CPU. Image: John Brecher for Microsoft

“Microsoft is building the infrastructure to support AI innovation, and we are reimagining every aspect of our data centers to meet the needs of our customers,” said Scott Guthrie, executive vice president of Microsoft’s Cloud + AI Group, in the press release. “At the scale we operate, it’s important for us to optimize and integrate every layer of the infrastructure stack to maximize performance, diversify our supply chain and give customers infrastructure choice.”

Sidekick server racks offer liquid cooling

To make room in data centers for the Microsoft Maia 100 AI Accelerator, Microsoft developed custom server racks. These sidekick racks are wider than the typical Microsoft server format and sit next to the Microsoft Maia 100 rack.

Liquid cooling fluid runs from the sidekick to the Maia 100 rack and back, creating a cooler environment. The custom racks could be used with silicon from industry partners.

New chips were designed for cloud workloads

Microsoft expects customers to use the new chips for AI and cloud computing from Microsoft’s data centers, including running Microsoft Copilot or Azure OpenAI Service. The Maia 100 and Cobalt 100 chips are made for custom racks within Microsoft data centers.

SEE: Microsoft adds a Copilot preview to Azure.

Microsoft has been steadily working on producing more and more of the components of the Microsoft Cloud itself. The silicon was the final piece of the puzzle.

“We have visibility into the entire stack, and silicon is just one of the ingredients,” said Rani Borkar, corporate vice president for Azure Hardware Systems and Infrastructure at Microsoft, in the press release.

Microsoft’s relationship to its silicon competitors

Offering in-house chips prevents Microsoft from having to rely on competitors to run large AI workloads. The Maia chip in particular could compete with NVIDIA’s AI-focused GPUs. AMD, Arm, AWS, Intel, Meta, Google, SambaNova and Qualcomm also all produce chips meant for AI workloads.

SEE: NVIDIA revealed new chips for AI and high-performance computing workloads.

Borkar told The Verge he doesn’t see the AI chip landscape as competition, but rather that Microsoft’s chips can be “complementary” to its partnerships, including those with other companies in the AI chip space.

“All the things we build, whether infrastructure or software or firmware, we can leverage whether we deploy our chips or those from our industry partners,” said Pat Stemen, partner program manager on the AHSI team, in the press release. “This is a choice the customer gets to make, and we’re trying to provide the best set of options for them, whether it’s for performance or cost or any other dimension they care about.”

Microsoft is not planning on replacing any existing hardware from AMD, Intel or NVIDIA. Instead, the company frames the first-party silicon option as giving customers more choices.

Microsoft plans to produce second-generation versions of both the Maia and Cobalt chips at an unspecified time in the future.

Tuning the Linux kernel with AI, according to ByteDance

AI-enhanced penguin

Richmond, Virginia: At the Linux Plumbers Conference, the invite-only meeting for the top Linux kernel developers, ByteDance Linux Kernel Engineer Cong Wang, proposed that we use artificial intelligence (AI) and machine learning (ML) to tune the Linux kernel for the maximum results for specific workloads.

Also: Rust in Linux: Where we are and where we're going next

Generally speaking, the Linux kernel works just fine for most tasks. But, to get the most from it for a particular job, you must fine-tune it by setting its parameters for the best possible results. There's only one little problem with this approach. There are thousands of parameters. Even for a Linux expert, tuning them for optimal performance is a long, hard job. And, of course, different workloads require different tunings for different sets of Linux kernel parameters. Thus, as Wang said, "In large-scale data centers like ByteDance's, it has become nearly impossible to tune Linux kernel parameters manually for hundreds of different workloads."

Tools such as System Management Interface Tool (SMIT), Sysctl, and TuneD can help. But they just enable you to make manual kernel tuning changes more easily. There are also "smart" programs, such as Red Hat's BayOp, that use ML to optimize network application efficiency specifically, but it's not a general-purpose AI/ML program; it's meant for a specific kind of Linux tuning.

Also: Garuda KDE Dr460nized might be the coolest-looking Linux distro available

What ByteDance is working on is a first attempt to automate the entire Linux kernel parameter tuning process with minimal engineering efforts. Specifically, ByteDance is working on tuning Linux memory management. ByteDance has found that with machine learning algorithms, such as Bayesian optimization, automated tuning could even beat most Linux kernel engineers.

Why? Well, the idea, as Wang wryly put it, "is not to put Linux kernel engineers out of business." No, the goal is "to liberate human engineers from tuning performance for each individual workload. While making better decisions with historical data, which humans often struggle with. And, last, but never least, find better solutions than those we come up with using our current trial and error, heuristic methods.

Also: If you want to try BSD, go with GhostBSD

How? The autotuning system is designed to automatically adjust the Linux kernel's internal settings based on the specific workload and hardware configuration. This dynamic adjustment ensures optimal performance, addressing a long-standing challenge in the Linux community of manually tuning the kernel for specific scenarios. To do this, the AI/ML framework uses multiple algorithms such as Bayesian Optimization, Genetic Algorithm, and the Simulated Annealing/Evolutionary Algorithm

The result?

  • Dynamic Optimization: The system continuously monitors the kernel's performance, making real-time adjustments to settings such as CPU frequency scaling and memory management.

  • Enhanced Efficiency: By optimizing resource usage, the autotuning system significantly improves the efficiency of Linux systems, particularly in environments with varying workloads.

  • User-Friendly Interface: The system includes a user-friendly interface, allowing even those with limited technical knowledge to benefit from enhanced kernel performance.

  • Customizable Settings: Advanced users can customize the autotuning parameters, tailoring the system to their specific needs.

It's still early days, but ByteDance is already seeing some success. For example, by using DAMON, a Linux kernel subsystem for memory access monitoring and optimization, with the framework, they were able to find the best scheme for a MySQL application. It did this by running different DAMON schemes and comparing their performance. They found they could reduce the application's memory usage by 30%. For massive applications, that's a real savings.

Also: Linux might be your best bet for heightening your desktop computer security

In another case, ByteDance was able to optimize HTTP network latency on an NGINX server by optimizing the tuning of 16 kernel sysctl parameters. In its best scenario, the ML tuning gave the NGNIX network performance a 12% boost over expert manual tuning. Again, that's a significant improvement.

ByteDance isn't claiming its AI/ML approach will work for every Linux tuning job, but Wang did say, "Although there are limitations, we believe that kernel machine learning is not only possible but also necessary."

Me? I think this is a potential game-changer for Linux applications. By simplifying the kernel optimization, it will make Linux more accessible and efficient for a broader range of users and applications. In particular, I see the autotuning system kicking up the performance on almost all servers, cloud computing, and data center applications.

Linux

Microsoft brings Copilot to Windows 10

Microsoft brings Copilot to Windows 10 Kyle Wiggers 14 hours

Copilot, Microsoft’s AI-powered chatbot experience, is heading to Windows 10 in preview

Starting with Windows 10 version 22H2, which will be available soon to users participating in Microsoft’s Windows Insider program, Windows 10 will gain more or less the same Copilot capabilities as Windows 11. Users will be able to ask Copilot questions and for suggestions pertaining to any number of tasks and topics, interacting with the chatbot either by typing or speaking after clicking the Copilot icon on the taskbar.

It’s a slightly neutered experience compared to Copilot on Windows 11. Copilot on Windows 10 can’t take actions like customizing preferences and settings or opening apps — at least not yet. Microsoft hints in a blog post that this functionality could arrive sometime down the line.

Copilot in Windows 10

Image Credits: Microsoft

Why bring Copilot to Windows 10? Well, volume, mainly. As Windows Central notes in a recent piece, Windows 10 is still in use on around a billion monthly active devices compared to the 400 million monthly active devices on Windows 11. It’s likely to be easier to incentivize developers to create Copilot plug-ins, which launched in May, when they can target both install bases versus one smaller one.

Windows 10 with Copilot is rolling out for devices running Windows 10 Home and Windows 10 Pro with at least 4GB of RAM and a 720p or higher display. Enterprise, Education and company-managed Pro editions of Windows 10 aren’t included in this upgrade cycle, nor are customers in Europe; only Windows 10 devices in North America and parts of Asia and South America are eligible for Copilot.

Microsoft says that it’s its intention to add additional markets over time.

Menlo Ventures closes on $1.35B in new capital, targets investments in AI startups

Menlo Ventures closes on $1.35B in new capital, targets investments in AI startups Mary Ann Azevedo 10 hours

Menlo Ventures has raised $1.35 billion in new capital that it plans to use in part “to support the forthcoming generation of AI startups,” the venture firm announced today.

Notably and unsurprisingly, the firm is today quite bullish on the future of artificial intelligence, noting that it has already backed the likes of Abnormal, Anthropic, Cleanlab, Pinecone and Typeface.

“Not every investment has to be an AI investment, but we believe that’s where the most exciting innovations will spark,” Menlo Ventures Partner Venky Ganesan said in an emailed statement. “That said, we also believe that, as AI becomes more sophisticated, we will increasingly rely on AI-powered tools and services to make us faster, smarter, and better at what we do. At that point, AI will lose its novelty and become an unsurprising, if not expected, collaborator throughout the day. Many of the companies that were already in the market with products and services that lacked AI features have moved quickly to layer in AI capabilities so that they can remain competitive and relevant in this new AI era.”

With the new funds, the firm says it has raised more than $3.8 billion across eight fund groups and distributed $5.2 billion to its LPs. The new capital will be invested out of the 46-year-old firm’s flagship venture fund, Menlo XVI, which is focused on the seed to Series A stages; Menlo Inflection III, which is focused on Series B and beyond; and their affiliated funds.

In July of 2022, the Bay Area firm, known for its early bets on companies like Uber and Warby Parker, told regulators that it had secured $761.4 million for its third “special opportunities” fund. Menlo closed on Fund XV, a $500 million raise, in October of 2020. It currently has $5.6 billion under management.

Over the years, Menlo has seen 80 portfolio companies exit, 15 of which went public. Those companies include Getaround, Carbonite, Gilead, Roku and Rover.

The firm also says it made seed and early-stage investments in 24 unicorns across the enterprise, consumer and healthcare sectors, and saw 65 of its portfolio companies get acquired. Those acquired companies include StrataCam (bought by Cisco), Tenor (acquired by Google) and PillPack (acquired by Amazon), among others.

Recent Menlo investments include leading Finch’s $40 million Series B raise and Sana Labs’ $34 million round. It also — as mentioned above — participated in AI darling Anthropic’s May $450 million raise.

Looking ahead, the firm said in a blog post: “AI represents a seismic shift that will add trillions of dollars in value to the global economy, and Menlo will help write the next chapter.” However, it intends to still invest in healthcare/digital health, consumer, cloud infrastructure, cybersecurity, fintech and SaaS startups as well.

Menlo isn’t the only venture firm raising $1 billion-plus funds these days. In October, Greylock Partners unveiled two new endeavors: a $1 billion early-stage fund — its 17th — and Greylock Edge, a program to support founders developing ideas into companies with early revenue and product market fit.

This story was updated post-publication to include comments and information from Menlo.

Windows 10 PCs will get Copilot AI chatbot after all

windows10-gettyimages-1237969724

When Microsoft's AI-powered Copilot for Windows was announced last May, the company made clear that its hot new chat-based tool would be exclusively for devices running Windows 11. Indeed, upon its formal release in September, the Copilot feature wasn't available on PCs running Windows 10.

Also: What are Microsoft's different Copilots? Here's how you can use them

That's all about to change, as Microsoft has done a 180 and plans to introduce a preview version of Copilot for Windows 10. When it's released, the new Copilot for Windows 10 (preview) will be available only to owners of unmanaged PCs running Windows 10 Home and Pro, version 22H2. Even then, it will only go to only PCs that are enrolled in the Release Preview channel of the Windows Insider Program and have "Get the latest updates as soon as they're available" enabled in Settings > Update & Security > Windows Update.

The feature also requires 4GB of RAM and a minimum display resolution of 720p, which will exclude a small number of older, underpowered PCs that wouldn't be able to comfortably run the Copilot sidebar.

Copilot in Windows 10 (preview) resembles its Windows 11 counterpart.

Enterprise administrators can relax, at least for now. The initial rollout of Copilot for Windows 10 won't be available for devices running Windows 10 Enterprise and Education editions, nor will it be offered on machines running Windows 10 Home or Pro that are managed using Windows Update for Business, Windows Software Update Services, or mobile device management (MDM) software.

At first glance, the sudden change in course for Microsoft is odd. Why spend precious development resources releasing a new feature on a platform that is less than two years from its end-of-support deadline? With Microsoft's full-court press to bring Copilot to the masses, it finally dawned on senior management in Redmond that turning away several hundred million customers was a bad idea.

Also: The best Windows laptop you can buy

This explanation appears in an unsigned support article, "How we are maximizing value in Windows 10," which was published today:

We are constantly evaluating how we adapt the latest technological advances to best meet people's needs. Over the past year, the rapid pace of innovation in AI has reshaped the technology landscape and we are reevaluating all our products through that lens, including Windows. Just as the PC increased in value and importance to each of our lives through the COVID pandemic, we believe this new era of AI increases the value and importance of the PC further, with Copilot in Windows at the center.

We are hearing great feedback on Copilot in Windows (in preview) and we want to extend that value to more people. For this reason, we are revisiting our approach to Windows 10 and will be making additional investments to make sure everyone can get the maximum value from their Windows PC including Copilot in Windows (in preview).

Based on a mockup version of the new feature supplied by Microsoft, Copilot on Windows 10 PCs will resemble its Windows 11 counterpart, with a taskbar button that appears to the right of the search box; clicking that button opens the Copilot chat sidebar, which appears on the right side of the display.

Also: Windows 11 FAQ: ZDNET's upgrade guide and everything else you need to know

Members of the Windows Insider Program who enable the option to "Get the latest updates as soon as they're available" will see the Copilot preview first. For the remaining PCs running Windows 10, Insider or otherwise, Microsoft says the feature will be delivered as "a controlled feature rollout (CFR) over the coming months."

Meta brings us a step closer to AI-generated movies

Meta brings us a step closer to AI-generated movies Kyle Wiggers 8 hours

Like “Avengers” director Joe Russo, I’m becoming increasingly convinced that fully AI-generated movies and TV shows will be possible within our lifetimes.

A host of AI unveilings over the past few months, in particular OpenAI’s ultra-realistic-sounding text-to-speech engine, have given glimpses into this brave new frontier. But Meta’s announcement today put our AI-generated content future into especially sharp relief — for me at least.

Meta his morning debuted Emu Video, an evolution of the tech giant’s image generation tool, Emu. Given a caption (e.g. “A dog running across a grassy knoll”), image or a photo paired with a description, Emu Video can generate a four-second-long animated clip.

Emu Video’s clips can be edited with a complementary AI model called Emu Edit, which was also announced today. Users can describe the modifications they want to make to Emu Edit in natural language — e.g. “the same clip, but in slow motion” — and see the changes reflected in a newly generated video.

Now, video generation tech isn’t new. Meta’s experimented with it before, as has Google. Meanwhile, startups like Runway are already building businesses on it.

But Emu Video’s 512×512, 16-frames-per-second clips are easily among the best I’ve seen in terms of their fidelity — to the point where my untrained eye has a tough time distinguishing them from the real thing.

Emu Video

Image Credits: Meta

Well — at least some of them. It seems Emu Video is most successful animating simple, mostly static scenes (e.g. waterfalls and timelapses of city skylines) that stray from photorealism — that is to say in styles like cubism, anime, “paper cut craft” and steampunk. One clip of the Eiffel Tower at dawn “as a painting,” with the tower reflected in the River Seine beneath it, reminded me of an e-card you might see on American Greetings.

Emu Video

Image Credits: Meta

Even in Emu Video’s best work, however, AI-generated weirdness manages to creep in — like bizarre physics (e.g. skateboards that move parallel to the ground) and freaky appendages (toes legs that curl behind feet and legs that blend into each other). Objects often appear and fade from view without much logic to it, too, like the birds overhead in the aforementioned Eiffel Tower clip.

After much too much time spent browsing Emu Video’s creations (or at least the examples that Meta cherry-picked), I started to notice another obvious tell: subjects in the clips don’t… well, do much. So far as I can tell, Emu Video doesn’t appear to have a strong grasp of action verbs, perhaps a limitation of the model’s underpinning architecture.

Emu Video

Image Credits: Meta

For example, a cute anthropomorphized racoon in an Emu Video clip will hold a guitar, but it won’t strum the guitar — even if the clip’s caption included the word “strum.” Or two unicorns will “play” chess, but only in the sense that they’ll sit inquisitively in front of a chessboard without moving the pieces.

Emu Video

Image Credits: Meta

So clearly there’s work to be done. Still, Emu Video’s more basic b-roll wouldn’t be out of place in a movie or TV show today, I’d say — and the ethical ramifications of this frankly terrify me.

Emu Video

Image Credits: Meta

The deepfakes risk aside, I fear for animators and artists whose livelihoods depend on crafting the sorts of scenes AI like Emu Video can now approximate. Meta and its generative AI rivals would likely argue that Emu Video, which Meta CEO Mark Zuckerberg says is being integrated into Facebook and Instagram (hopefully with better toxicity filters than Meta’s AI-generated stickers), augment rather than replace human artists. But I’d say that’s taking the optimistic, if not disingenuous, view — especially where money’s involved.

Earlier this year, Netflix used AI-generated background images in a three-minute animated short. The company claimed that the tech could help with anime’s supposed labor shortage — but conveniently glossed over how low pay and often strenuous working conditions are pushing away artists from the work.

In a similar controversy, the studio behind the credit sequence for Marvel’s “Secret Invasion” admitted to using AI, mainly the text-to-image tool Midjourney, to generate much of the sequence’s artwork. Series director Ali Selim made the case that the use of AI fits with the paranoid themes of the show, but the bulk of the artist community and fans vehemently disagreed.

Emu Video

Image Credits: Meta

Actors could be on the chopping block, too. One of the major sticking points in the recent SAG-AFTRA strike was the use of AI to create digital likenesses. Studios ultimately agreed to pay actors for their AI-generated likenesses. But might they reconsider as the tech improves? I think it’s likely.

Adding insult to injury, AI like Emu Video is usually trained on images and videos produced by artists, photographers and filmmakers — and without notifying or compensating those creators. In a whitepaper accompanying the release of Emu Video, Meta says only that the model was trained on a data set of 34 million “video-text pairs” ranging in length from 5 to 60 seconds — not where those videos came from, their copyright statuses or whether Meta licensed them.

Emu Video

Image Credits: Meta

There’s been fits and starts toward industry-wide standards to allow artists to “opt out” of training or receive payment for AI-generated works to which they contributed. But if Emu Video is any indication, the tech — as so often happens — will soon run far ahead of ethics. Perhaps it already has.

An Ethical Valve for GenAI Pipeline 

An Ethical Valve for GenAI Pipeline 

In an Era of GenAI, while leveraging various benefits across the use cases and domains, its ethical concerns are dimming the charm of GenAI. Consequently, the most crucial consideration is its ethical and responsible aspects. GenAI usage pipeline should be restricted by an ethical valve in order to leverage it effectively and productively.

Now the question is, what are the ingredients that should be considered while creating an effective ethical valve?

Although the ethical protocols could be universal across the domain, various domains have different vulnerable gateways. Considering a digital healthcare domain or education domain, accessibility should be linked with an identity card for the allocation of facilities and privileges. Age factor should be a consideration factor so that GenAI usage should not kill growing human creativity and thinking abilities.

From a perspective, ethical principles and values should be an integral part of GenAI usage. Transparency of models, their limitations and their impact should be very well described by the providers or facilitators.

An Ethical valve framework could be designed broadly, considering major concerns:

✔ Biases Valve:

Implementing ongoing training and evaluation to mitigate biases in responses can help ensure fair and unbiased AI interactions.

✔ Accountability valve:

Commitment and responsibility of actions; to addressing issues and concerns raised by users and the wider community.

✔ Consent valve:

Implementing consent features; the choice to engage with AI-generated content.

✔ Transparency Valve:

Potential risks and limitations of models used; reducing the potential for misinformation.

✔ Privacy and security valve:

Implementing strong content filtering, monitoring, and flagging systems can help prevent the spread of harmful information. Personal information shared during interactions should be handled with care and data protection regulations.

✔ Feedback-learning back valve:

Establish mechanisms for users and the public to quickly provide feedback, report concerns and learn back.

These valves can help the developers develop an ethical-empathetic code, not just a logical programming code.

Ethical development and deployment of AI models could be an integral part of the foundation of GenAI applications across the domain. Continuous collaboration between developers, product managers and customer experience experts should be required to do so for the community and societal technological well-being.

It’s important to strike a balance between innovation and responsible AI use to ensure AI technologies are leveraged in the most productive manner without any ethical concerns.

A Way to Address Ethical Concerns in the GenAI World

Develop an ethical expert system as an ethical valve for the system’s pipeline across the domain

Combining all the major components of the ethical framework described above, Creating a rule-based AI expert for ethical consideration involves developing a system that follows predefined rules and guidelines to evaluate the ethical aspects of various scenarios and decisions. Here’s a basic outline of how such an AI ethical expert might be structured:

✔ Rule Database:

Create a comprehensive database of ethical rules, principles, and guidelines. These rules should encompass various ethical frameworks, such as utilitarianism, deontology, and virtue ethics, and address specific domains like healthcare, finance, education or AI development.

✔ Input Interface:

Develop an interface through which users can input information about a specific scenario or decision that requires ethical consideration. This could be in the form of text, structured data, or a series of questions.

✔ Rule-Based Inference Engine:

Design an inference engine that processes the input data and matches it against the rule database. The engine should identify relevant ethical rules and principles based on the input.

✔ Ethical Evaluation:

The AI ethical expert should evaluate the ethical implications of the scenario based on the identified rules. It can provide insights into whether the decision aligns with ethical principles or if there are potential ethical concerns.

✔ Recommendations:

Based on the ethical evaluation, the AI ethical expert can offer recommendations for actions to take or alternative approaches that may be more ethically sound.

✔ Explanations:

To enhance transparency, the AI should provide explanations for its ethical evaluations and recommendations. Users should understand why a particular decision is considered ethical vs unethical.

✔ User Interaction:

Implement a user-friendly interface for interacting with the AI ethical expert. Users should be able to ask questions, clarify inputs, or seek further information.

✔ Continuous Learning:

Allow the AI to learn and adapt over time i.e. AI based ethical learner agent. As new ethical rules and guidelines emerge, the system should be updated to stay current and relevant.

✔ Monitoring and Auditing:

Regularly monitor the AI’s performance and conduct audits to ensure that it continues to align with evolving ethical standards. Correct any biases or inaccuracies in the rule database.

✔ Ethical Safeguards:

Implement safeguards to prevent misuse of the AI expert for unethical purposes. Ensure that it adheres to responsible AI guidelines and does not enable harmful decisions.

✔ User Feedback Loop:

Establish a feedback mechanism for users to report concerns, inaccuracies, or issues related to ethical considerations. Use this feedback to improve the AI’s performance.

✔ Collaboration with Ethical Experts:

Collaborate with ethicists, philosophers, and domain-specific experts to refine the rule database and improve the AI’s understanding of complex ethical issues.

Building a rule-based AI expert for ethical considerations is a challenging but important endeavour, as it can assist individuals and organizations in making ethically informed decisions across various domains. It’s essential to continually update and improve the system to ensure its effectiveness and relevance in a rapidly evolving ethical landscape.

The post An Ethical Valve for GenAI Pipeline appeared first on Analytics India Magazine.

Why are VCs Getting Generative AI Wrong

Recently, more than 35 VC firms and over 15 companies have committed to responsible AI, guided by Responsible Innovation Labs (RIL). To support these commitments, a 15-page Responsible AI Protocol has been published, providing practical guidance for both investors and startups.

While Responsible AI is not a bad idea, many in Silicon Valley believe it could slow down the pace of development. This is just one example of how VCs are entering the picture and interfering with the ecosystem.

This is not the first time. VCs always get tech wrong. A report by Menlo Ventures has been circulating on social media, stating that enterprises spent approximately $2.5 billion on generative AI in 2023. The report further added that the market remains nascent.

Breaking it Down

Menlo’s report compares an estimated $2.5 billion spent by enterprises on generative AI in 2023, to traditional AI ($70 billion) and cloud software ($400 billion). Surprisingly, these figures are presented without a clear breakdown or context, making it challenging to assess the accuracy or relevance of the comparisons.

Moreover, it does not make sense at all, as cloud is a crucial element of generative AI, and one cannot just segregate it like that. Interestingly, earlier this year, Menlo had said that they expect generative AI to eat into—and expand—the entirety of today’s $140 billion consumer and enterprise software markets, hence contradicting its own words.

On the other hand, investment bank Goldman Sachs says generative AI could make work much more efficient, increasing productivity by over 1% yearly after widespread use. But, for this to happen, enterprises must invest around $200 billion globally by 2025 in technology and workforce.

It seems that every investment firm, based on its own research, is coming out with numbers that fail to match each other.

What VCs really think of generative AI

Lux Capital recently published a blog ‘How Many Creators Will Survive Generative AI?’. The blog suggests that while AI has limitations, it may replace a significant portion of the creative work done by humans. The author argues that much of the work in fields like journalism, marketing, and social media lacks originality and could be automated.

The Lux Capital report said, “About 90%, maybe even 98% of the creative class’ output can be replaced today with generative AI.” This is absolutely unnecessary and creates fear among the netizens.

Expressing similar sentiments, Vinod Khosla said, “If I were to look at 2040, about 15 to 20 years from now, I believe 80% of all jobs will be done by AI and better than humans.” Furthermore, he added, “I’m a huge optimist who realises that we will have to be dynamic in responding to its negative consequences, which will be there.”

On the flip side, in his latest blog, ‘The Techno-Optimist Manifesto,’ Marc Andreessen stated that the idea that technology takes our jobs, reduces our wages, and increases inequality is a lie spread by pessimists.

Meanwhile, Sequoia Capital published a blog stating that generative AI is now entering its Act 2, with the aim of solving human problems end to end. The blog suggests that new applications of AI are not like the initial ones. Enterprises now use foundation models as part of a larger solution, rather than the entire solution.

It seems that the tech ecosystem is confused and VCs are as new to generative AI as consumers. Thus, they may not be the best judge to realise the potential of new technology. Besides, there is another factor: The end goal of VCs is to make good returns, so there’s a chance they might manipulate the reports in their favour. Vinod Khosla, the founder of Khosla Ventures, the first investors of OpenAI, also offers a stern warning, suggesting that most AI investments today will lead to losses. He likens AI investing to a hype cycle, cautioning that only disciplined investors will reap the benefits.

The post Why are VCs Getting Generative AI Wrong appeared first on Analytics India Magazine.

Several popular AI products flagged as unsafe for kids by Common Sense Media

Several popular AI products flagged as unsafe for kids by Common Sense Media Sarah Perez @sarahintampa / 10 hours

An independent review of popular AI tools has found that many — including Snapchat’s My AI, DALLE, and Stable Diffusion, may not be safe for kids. The new reviews come from Common Sense Media, a nonprofit advocacy group for families that’s best known for providing media ratings for parents who want to evaluate the apps, games, podcasts, TV shows, movies, and books their children are consuming. Earlier this year, the company said it would soon add ratings for AI products to its resources for families. Today, those ratings have gone live, offering so-called “nutrition labels” for AI products, like chatbots, image generators, and more.

The company first announced in July that it aimed to build a ratings system to assess AI products across a number of dimensions, including whether or not the technology takes advantage of responsible AI practices as well as its suitability for children. The move was triggered by a survey of parents to gauge their interest in such a service. 82% of parents said they wanted help in evaluating whether or not new AI products, like ChatGPT, were safe for their kids to use. Only 40% said they knew of any reliable resources that would help them to make those determinations.

That led to today’s launch of Common Sense Media’s first AI product ratings. The products it assesses are ratings across several AI principles, including trust, kids’ safety, privacy, transparency, accountability, learning, fairness, social connections, and benefits to people and society.

The organization initially reviewed 10 popular apps on a 5-point scale, including those used for learning, AI chatbots like Bard and ChatGPT, as well as generative AI products, like Snap’s My AI and DALL-E and others. Not surprisingly, the latter category fared the worst.

“AI is not always correct, nor is it values-neutral,” remarked Tracy Pizzo-Frey, Senior Advisor of AI at Common Sense Media, in a summary of the ratings. “All generative AI, by virtue of the fact that the models are trained on massive amounts of internet data, host a wide variety of cultural, racial, socioeconomic, historical, and gender biases – and that is exactly what we found in our evaluations,” she said. “We hope our ratings will encourage more developers to build protections that limit misinformation from spreading and do their part to shield future generations from unintended repercussions.”

In TechCrunch’s own tests, reporter Amanda Silberling found Snapchat’s My AI generative AI features generally tended to be more weird and random, than actively harmful, but Common Sense Media gave the AI chatbot a 2-star rating, noting that it produced some responses that reinforced unfair biases around ageism, sexism, and cultural stereotypes. It also offers some inappropriate responses, at times, as well as inaccuracies. It also stored personal user data, which the organization said raised privacy concerns.

Snap pushed back at the poor review, noting that My AI was an optional tool and that Snapchat makes it clear it’s a chatbot and advises users about its limitations.

“By default, My AI displays a robot emoji. Before anyone can interact with My AI, we show an in-app message to make clear it’s a chatbot and advise on its limitations,” said Snap spokesperson, Maggie Cherneff. “My AI is also integrated into our Family Center so parents can see if and when teens are chatting with it. We appreciate the feedback in the review as we continue to improve our product,” she added.

Other generative AI models like DALL-E and Stable Diffusion had similar risks, including a tendency toward objectification and sexualization of women and girls and a reinforcement of gender stereotypes, among other concerns. (Requests for comment were not immediately returned.)

Like any new medium on the internet, these generative AI models are also being used to produce pornographic materials. Sites like Hugging Face and Civitai have grown popular not only as resources for finding new image models, but also making it easier to find different models that can be combined with one another to make porn using someone’s (like a celeb’s) likeness. That issue came to a head this week, as 404Media called out Civitai’s capabilities, but the debate as to the responsible party — the community aggregators or the AI models itself — continued on sites like Hacker News in the aftermath.

In the mid-tier of Common Sense’s ratings, were AI chatbots like Google’s Bard (which just yesterday officially opened to teens), ChatGPT, and Toddle AI. The organization warned that bias may occur in these bots as well, particularly for users with “diverse backgrounds and dialects.” They could also produce inaccurate information — or AI hallucinations — and reinforce stereotypes. Common Sense warned that the false information AI produces could shape users’ worldviews and make it even more difficult to separate fact from fiction.

The only AI products to receive good reviews were Ello’s AI reading tutor and book delivery service, Khanmingo (from Khan Academy), and Kyron Learning’s AI tutor — all three being AI products designed for educational purposes. They’re less well-known than others. (And, as some kids may argue, less fun). Still, because the companies designed them with kids’ usage in mind, they tended to use responsible AI practices and focused on fairness, diverse representation, and kid-friendly design considerations. They also were more transparent about their data privacy policies.

Common Sense Media says it will continue to publish ratings and reviews of new AI products on a rolling basis, which it hopes will help to inform not only parents and families, but also lawmakers and regulators.

“Consumers must have access to a clear nutrition label for AI products that could compromise the safety and privacy of all Americans—but especially children and teens,” said James P. Steyer, founder and CEO of Common Sense Media, in a statement. “By learning what the product is, how it works, its ethical risks, limitations, and misuses, lawmakers, educators, and the general public can understand what responsible AI looks like. If the government fails to ‘childproof’ AI, tech companies will take advantage of this unregulated, freewheeling atmosphere at the expense of our data privacy, well-being, and democracy at large,” he added.

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