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In the past two weeks I visited two schools in Veneto to engage students with the topic of Artificial Intelligence, which is something everybody seems to be happy to hear about these days: on the 10th of January I visited a school in Vicenza, and on the 17th a school in Venice. In both cases there were about 50-60 students, but there was a crucial difference: while the school in Venezia (the "Liceo Marco Foscarini", where I have been giving lectures in the past within the project called "Art and Science") was a classical liceum and the high-schoolers who came to listen to my presentation were between 16 and 18 years old, the one in Vicenza was a middle school, and its attending students were between 11 and 13 years old.Since the contents of the lecture could withstand virtually no change - I was too busy during these first few post-Christmas weeks - the two-pronged test was an effective testing ground to spot differences in the reaction of the two audiences. To be honest, I approached the first event with some worries that the content I was presenting to those young kids was going to be a bit overwhelming to them, so maybe in hindsight we could imagine that the impression I got was biased by this "low expectations" attitude.

To make matters worse, because my lecture was the first in a series organized by a local academy, with comparticipation of the Comune of Vicenza, the lecture I gave had to follow speeches from the school director, the maior of Vicenza, and a couple of other introductions - something that I was sure was further decreasing the stamina and willingness to listen to a frontal lecture of the young audience. In fact, I was completely flabberghasted.

Not only did the middle schoolers in Vicenza follow with attention and in full silence the 80-minutes-long talk I had prepared. They also interrupted a few times with witty questions (as I had begged them to do, in fact). At the end of the presentation, I was hit by a rapid succession of questions ranging over the full contents of the lecture - from artificial intelligence to particle physics, to details about the SWGO experiment, astrophysics, and what not. I counted about 20 questions and then lost track of that. This continued after the end of the event, when some of the students were not completely happy yet and came to meet me and ask for more detail.

Above, a moment during the lecture in Vicenza

When I gave the same lecture in Venice, I must say I did receive again several interesting questions. But in comparison, the Foscarini teenagers were clearly a bit less enthusiastic on the whole of the topic of the lecture. Maybe my assessment comes from the bias I was mentioning earlier; and in part, I have to say I have much more experience with high-schoolers than with younger students, so I knew better what to expect and I was not surprised by the outcome.

This comparison seems to align with what has been once observed by none other than Carl Sagan. I have to thank Phil Warnell here, who commenting on Facebook to a post I wrote there on my experience with middle schoolers cited a piece from Sagan that is quite relevant:

I cannot but concur with what Sagan says in these two quotes. I also believe that part of the unwillingness of high-schoolers to ask questions is due to the judgment of their peers. What happens is that until we are 12 or 13 we for the most part have not yet had experience with the negative feedback we may get by being participative in school events, and we do not yet fear the reaction of our friends and not-so-friendly schoolmates. It seems that kind of experience grows a shell around them, making them a bit less willing to expose themselves and speak up to discuss what they did not understand, or to express enthusiasm. I think that is a bit sad, but it is of course part of our early trajectory amid experiences that form us and equip us with the vaccines we are going to need in the rest of our life.

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Comparing Student Reactions To Lectures In Artificial Intelligence And Physics - Science 2.0

By David Nolan, Hajira Maryam & Michael Kleinman, Amnesty Tech

The year 2023 marked a new era of AI hype, rapidly steering policy makers towards discussions on the safety and regulation of new artificial intelligence (AI) technologies. The feverish year in tech started with the launch of ChatGPT in late 2022 and ended with a landmark agreement on the EU AI Act being reached. Whilst the final text is still being ironed out in technical meetings over the coming weeks, early signs indicate the western worlds first AI rulebook goes someway to protecting people from the harms of AI but still falls short in a number of crucial areas, failing to ensure human rights protections especially for the most marginalised. This came soon after the UK Government hosted an inaugural AI Safety Summit in November 2023, where global leaders, key industry players, and select civil society groups gathered to discuss the risks of AI. Although the growing momentum and debate on AI governance is welcomed and urgently needed, the key question for 2024 is whether these discussions will generate concrete commitments and focus on the most important present-day AI risks, and critically whether it will translate into further substantive action in other jurisdictions.

Whilst AI developments do present new opportunities and benefits, we must not ignore the documented dangers posed by AI tools when they are used as a means of societal control, mass surveillance and discrimination. All too often, AI systems are trained on massive amounts of private and public datadata which reflects societal injustices, often leading to biased outcomes and exacerbating inequalities. From predictive policing tools, to automated systems used in public sector decision-making to determine who can access healthcare and social assistance, to monitoring the movement of migrants and refugees, AI has flagrantly and consistently undermined the human rights of the most marginalised in society. Other forms of AI, such as fraud detection algorithms, have also disproportionately impacted ethnic minorities, who have endured devastating financial problems as Amnesty International has already documented, while facial recognition technology has been used by the police and security forces to target racialised communities and entrench Israels system of apartheid.

So, what makes regulation of AI complex and challenging? First, there is the vague nature of the term AI itself, making efforts to regulate this technology more cumbersome. There is no widespread consensus on the definition of AI because the term does not refer to a singular technology and rather encapsulates a myriad technological applications and methods. The use of AI systems in many different domains across the public and private sector, means a large number of varied stakeholders are involved in its development and deployment, meaning such systems are a product of labour, data, software and financial inputs and any regulation must grapple with upstream and downstream harms. Further, these systems cannot be strictly considered as hardware or software, but rather their impact comes down to the context in which they are developed and implemented and regulation must take this into account.

As we enter 2024, now is the time to not only ensure that AI systems are rights respecting by design, but also to guarantee that those who are impacted by these technologies are not only meaningfully involved in decision-making on how AI technology should be regulated, but also that their experiences are continually surfaced and are centred within these discussions.

Alongside the EU legislative process, the UK, US, and others, have set out their distinct roadmaps and approach to identifying the key risks AI technologies present, and how they intend to mitigate these. Whilst there are many complexities of these legislative processes, this should not delay any efforts to protect people from the present and future harms of AI, and there are crucial elements that we, at Amnesty, know any proposed regulatory approach must contain. Regulation must be legally binding and center the already documented harms to people subject to these systems. Commitments and principles on the responsible development and use of AIthe core of the current pro-innovation regulatory framework being pursued by the UKdo not offer an adequate protection against the risks of emerging technology and must be put on statutory footing.

Similarly, any regulation must include broader accountability mechanisms over and above technical evaluations that are being pushed by industry. Whilst these may be a useful string within any regulatory toolkits bow, particularly in testing for algorithmic bias, bans and prohibitions cannot be off the table for systems fundamentally incompatible with human rights, no matter how accurate or technically efficacious they purport to be.

Others must learn from the EU process and ensure there are not loopholes for public and private sector players to circumvent regulatory obligations, and removing any exemptions for AI used within national security or law enforcement is critical to achieving this. It is also important that where future regulation limits or prohibits the use of certain AI systems in one jurisdiction, no loopholes or regulatory gaps allow the same systems to be exported to other countries where they could be used to harm the human rights of marginalized groups. This remains a glaring gap in the UK, US, and EU approaches, as they fail to take into account the global power imbalances of these technologies, especially their impact on communities in the Global Majority whose voices are not represented in these discussions. There have already been documented cases of outsourced workers being exploited in Kenya and Pakistan by companies developing AI tools.

As we enter 2024, now is the time to not only ensure that AI systems are rights-respecting by design, but also to guarantee that those who are impacted by these technologies are not only meaningfully involved in decision-making on how AI technology should be regulated, but also that their experiences are continually surfaced and are centred within these discussions.More than lip service by lawmakers, we need binding regulation that holds companies and other key industry players to account and ensures that profits do not come at the expense of human rights protections. International, regional and national governance efforts must complement and catalyse each other, and global discussions must not come at the expense of meaningful national regulation or binding regulatory standards these are not mutually exclusive. This is the level at which accountability is servedwe must learn from past attempts to regulate tech, which means ensuring robust mechanisms are introduced to allow victims of AI-inflicted rights violations to seek justice.

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The Urgent but Difficult Task of Regulating Artificial Intelligence - Amnesty International

As the company supplying 80% of the necessary training chips, Nvidia was arguably the biggest winner in 2023's artificial intelligence (AI) boom. That said, it makes sense for investors to diversify their holdings to target different sides of the long-term opportunity. Let's look at why Alphabet (GOOG -0.10%) (GOOGL -0.20%) and Meta Platforms (META -0.38%) could also have a place in your portfolio in 2024 and beyond.

With a market cap of $1.79 trillion, Alphabet is already the fourth-largest company in the world, and it will take a lot of momentum to power continued expansion. But AI may be able to do the trick. The tech giant is heavily incorporating AI infrastructure into its cloud-computing platform, which could generate much-needed diversification and long-term growth.

Among AI companies, Nvidia is particularly successful because it targets the "picks and shovels" side of the opportunity, minimizing competition while maximizing the total addressable market for its products. Google is developing a similar strategy (albeit higher on the value chain) by turning Google Cloud into a one-stop shop for all its enterprise clients' data-management and AI training needs. And while Google isn't the only cloud-service provider employing this strategy, it has some key advantages.

Image source: Getty Images.

According to CEO Sundar Pichai, 70% of generative AI start-up unicorns use Google's infrastructure to train and run their models. This is a big vote of confidence in the platform's quality and price point. And Google plans to build on this advantage with proprietary AI chips (called tensor processing units), which can bring down costs through vertical integration and reduce the company's reliance on third-party suppliers like Nvidia.

Alphabet's low valuation is icing on the cake for investors. With a forward price-to-earnings (P/E) multiple of just 22, the stock is significantly cheaper than the NASDAQ 100's estimate of 29.

Following the release of ChatGPT in late 2022, Meta's share price has been on a tear, jumping a substantial 174% in the last 12 months alone. Investors are optimistic about the company's decision to pivot away from metaverse development to focus more on generative AI, which could optimize its advertising and improve its consumer-facing platforms.

At first glance, Meta has some clear advantages in its AI efforts. The social media giant's business model has always involved gathering and monetizing huge amounts of data. And generative AI opens another avenue for this strategy through large language models (LLMs), which are algorithms designed to create content out of trained datasets.

Meta is also adding conversational AI experiences across its popular apps, introducing features ranging from more responsive image editing on Instagram to conversational chatbots with distinct personalities on WhatsApp. These efforts probably won't immediately impact Meta's operational performance, but they could help maintain its platforms' user engagement and generate valuable customer data.

On the operational side, Meta is bouncing back from the challenges it faced in 2022. Third-quarter (2023) revenue jumped by 23% year over year to $34.15 billion, while net income jumped 164% to $11.58 billion, helped by aggressive cost cutting and layoffs. And with a forward P/E of just 22, it isn't too late for investors to bet on the company's long-term potential.

In 2024 and beyond, investors should expect the AI landscape to become increasingly competitive, especially on the software side of the market. With that in mind, it makes sense to bet on companies with potential economic moats. Alphabet and Meta Platforms fit the bill because of their treasure troves of user data, which can be used to train and refine LLMs. Both companies look poised for market-beating growth.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fools board of directors. Randi Zuckerberg, a former director of market development and spokeswoman for Facebook and sister to Meta Platforms CEO Mark Zuckerberg, is a member of The Motley Fool's board of directors. Will Ebiefung has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Alphabet and Meta Platforms. The Motley Fool has a disclosure policy.

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Forget Nvidia: 2 Artificial Intelligence Stocks That Could Help Make You Rich in 2024 - The Motley Fool

The list of companies associated with artificial intelligence (AI) is growing quickly. While some of these newcomers are loosely associated with AI, others have a strong connection.

One that belongs in the conversation is Cloudflare (NET 4.33%) Its large data center footprint is something that nearly all companies with AI workloads can benefit from. But is Cloudflare a solid investment right now? Let's find out.

Cloudflare's original purpose was a content delivery network (CDN), which places information closer to the end user on the internet. If a website is based in the U.S., but someone wants to access it in India, it takes a long time for that information to travel the globe. (A long time in this instance might be seconds -- but that could cause problems for some uses.)

However, Cloudflare has strategically placed data centers in over 300 cities and 120 countries to put this content as close as possible to the end user, speeding up the process.

When companies choose to host on Cloudflare, it provides them with top-notch cybersecurity. Instead of everyone with a website needing their own security solution, hosting on Cloudflare centralizes the protection. This allows the company to develop and maintain protection better than most, making it a logical choice.

Its data centers can also be used for another purpose: generative AI. When running a generative AI program, you're once again limited by the proximity of the generative AI server. With Cloudflare, you can run the tasks on its networks, improving the model's efficiency with best-in-class cybersecurity.

Cloudflare is a great way to invest in a branch of cloud computing, an industry that's expected to grow significantly over the next decade. But does it make sense to buy the stock now?

It shouldn't surprise investors that a company like Cloudflare has a fair bit of hype around it. After all, its revenue grew 32% year over year in the third quarter, and it added nearly 30,000 customers over the past year, bringing its total to more than 182,000. Of that number, more than 2,500 pay $100,000 or more annually, up from 1,908 last year.

This all comes at a price, and Cloudflare's stock is highly valued.

NET PS ratio data by YCharts; PS = price to sales.

At 21 times sales, Cloudflare fetches a hefty premium to many of its tech peers. But is that warranted?

Cloudflare's long-term model projects an operating margin of 20% or more. If it could snap its fingers and achieve that with a tax rate of 20%, plus grow by 30% over the next three years, Cloudflare would produce hypothetical earnings of $425 million per year.

If you divide its current market cap ($26 billion) by that figure, you will get a forward price-to-earnings (P/E) ratio based on three-year projections. That comes out to 61 times earnings, which is a very expensive price to pay now, let alone for a company that must optimize its expenses and grow substantially for three years to achieve it.

Cloudflare could have multiple great periods over the next few years and succeed as a business, but the stock might not go anywhere due to the extremely high expectations already built into it.

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Is Cloudflare a Top Artificial Intelligence (AI) Stock in 2024? - The Motley Fool