Amazon (AMZN -0.63%) has dropped out of the spotlight this year as its big tech peers, Microsoftand Google parent Alphabet, fight an intense battle over artificial intelligence (AI).

Microsoft recently acquired a large stake in OpenAI, and it has integrated the ChatGPT chatbot into its Bing search engine and Azure cloud platform. Google has fired back with a chatbot of its own, called Bard.

But investors shouldn't ignore Amazon as a major player in this emerging industry, because Amazon Web Services (AWS) is the world's largest cloud platform, and the cloud is where most AI applications are developed and deployed.

Now, AWS plans to open a new program to support businesses in crafting their AI strategies, and it could be a major growth driver for the company going forward. Here's why it's time for investors to buy in.

Image source: Getty Images.

Artificial intelligence comes in many forms, and it's often used to ingest mountains of data in order to make predictions about future events. But generative AI is the version many consumers have become familiar with this year, and is capable of generating new content whether it's text, sound, images, or videos. Platforms like ChatGPT and Bard fall into that category.

OpenAI CEO Sam Altman says he's already seeing many businesses double their productivity using generative AI, and it has the potential to eventually deliver an increase of 30 times. It's because the technology can be prompted to instantly write computer code, or even generate creative works. It can also rapidly analyze thousands of pages of information and deliver answers to complex questions, which saves the user from scrolling through search engine results.

On Thursday, June 23, Amazon announced it was launching the AWS Generative AI Innovation Center with $100 million in funding. The program will connect businesses with AWS strategists, data scientists, engineers, and solution architects to help them design generative AI strategies and deploy the technology effectively and responsibly.

The program will provide no-cost workshops, engagements, and training to teach businesses how to use some of the most powerful AI tools available on AWS, like CodeWhisperer, which serves as a copilot for computer programmers to help them write software significantly faster.

Amazon says a handful of companies were already signed up, including customer engagement platform Twilio, which is using generative AI to help businesses provide deeper value to the people they serve.

AWS is one of the oldest data center customers of Nvidia, which currently produces the most powerful chips in the industry designed for AI workloads. The two companies recently signed a new deal to power AWS' new EC2 P5 infrastructure, which will allow its cloud customers to scale from 10,000 Nvidia H100 GPUs to 20,000, giving them access to supercomputer-like performance.

Overall, this could enable them to train larger AI language models than ever before, with far more precision and speed.

Here's my point: The more customers using that data center infrastructure, the more money AWS makes. Therefore, Amazon's $100 million investment in the Generative AI Innovation Center could result in multitudes of that amount coming back as revenue, as more businesses learn how to train and deploy AI. Also, the free training and access to expert engineers could make AWS an attractive on-ramp into the world of AI for many organizations, prompting them to shun other providers like Microsoft Azure and Google Cloud initially.

Estimates about the future value of the AI industry are wide-ranging, but staggering even at the low end. Research firm McKinsey & Company thinks the technology will add $13 trillion to global economic output by 2030, whereas Cathie Wood's Ark Investment Management places that figure at a whopping $200 trillion.

Therefore, it's no surprise tech giants are jostling for AI leadership, but AWS will approach the opportunity from a position of strength since it already sits atop the cloud industry.

Amazon is a very diverse business beyond the cloud; it also dominates e-commerce globally, has a fast growing digital advertising segment, and is a leader in streaming through its Prime and Twitch platforms. The company has generated $524 billion in revenue over the last four quarters, which is far more than Microsoft and Alphabet, yet Amazon stock trades at a cheaper price-to-sales (P/S) ratio than both of them.

Company

2022 Revenue (Billions)

Price-to-Sales Ratio

Amazon

$524

2.5

Microsoft

$208

12.0

Alphabet (Google)

$284

5.5

Source: Company filings.

As a result, investors can buy Amazon stock now at a very attractive valuation relative to its peers, which theoretically means it could deliver more upside in the long run. In fact, I think Amazon is on its way to a $5 trillion valuation within the next decade, and AI could supercharge its progress to reach that target even more quickly.

John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fools board of directors. Suzanne Frey, an executive at Alphabet, is a member of The Motley Fools board of directors. Anthony Di Pizio has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Alphabet, Amazon.com, Microsoft, Nvidia, and Twilio. The Motley Fool has a disclosure policy.

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In the absence of legal guidelines, companies need to establish internal processes for responsible use of AI. Oscar Wong/Moment via Getty Images

The rush to deploy powerful new generative AI technologies, such as ChatGPT, has raised alarms about potential harm and misuse. The laws glacial response to such threats has prompted demands that the companies developing these technologies implement AI ethically.

But what, exactly, does that mean?

The straightforward answer would be to align a businesss operations with one or more of the dozens of sets of AI ethics principles that governments, multistakeholder groups and academics have produced. But that is easier said than done.

We and our colleagues spent two years interviewing and surveying AI ethics professionals across a range of sectors to try to understand how they sought to achieve ethical AI and what they might be missing. We learned that pursuing AI ethics on the ground is less about mapping ethical principles onto corporate actions than it is about implementing management structures and processes that enable an organization to spot and mitigate threats.

This is likely to be disappointing news for organizations looking for unambiguous guidance that avoids gray areas, and for consumers hoping for clear and protective standards. But it points to a better understanding of how companies can pursue ethical AI.

Our study, which is the basis for a forthcoming book, centered on those responsible for managing AI ethics issues at major companies that use AI. From late 2017 to early 2019, we interviewed 23 such managers. Their titles ranged from privacy officer and privacy counsel to one that was new at the time but increasingly common today: data ethics officer. Our conversations with these AI ethics managers produced four main takeaways.

First, along with its many benefits, business use of AI poses substantial risks, and the companies know it. AI ethics managers expressed concerns about privacy, manipulation, bias, opacity, inequality and labor displacement. In one well-known example, Amazon developed an AI tool to sort rsums and trained it to find candidates similar to those it had hired in the past. Male dominance in the tech industry meant that most of Amazons employees were men. The tool accordingly learned to reject female candidates. Unable to fix the problem, Amazon ultimately had to scrap the project.

Generative AI raises additional worries about misinformation and hate speech at large scale and misappropriation of intellectual property.

Second, companies that pursue ethical AI do so largely for strategic reasons. They want to sustain trust among customers, business partners and employees. And they want to preempt, or prepare for, emerging regulations. The Facebook-Cambridge Analytica scandal, in which Cambridge Analytica used Facebook user data, shared without consent, to infer the users psychological types and target them with manipulative political ads, showed that the unethical use of advanced analytics can eviscerate a companys reputation or even, as in the case of Cambridge Analytica itself, bring it down. The companies we spoke to wanted instead to be viewed as responsible stewards of peoples data.

The challenge that AI ethics managers faced was figuring out how best to achieve ethical AI. They looked first to AI ethics principles, particularly those rooted in bioethics or human rights principles, but found them insufficient. It was not just that there are many competing sets of principles. It was that justice, fairness, beneficence, autonomy and other such principles are contested and subject to interpretation and can conflict with one another.

This led to our third takeaway: Managers needed more than high-level AI principles to decide what to do in specific situations. One AI ethics manager described trying to translate human rights principles into a set of questions that developers could ask themselves to produce more ethical AI software systems. We stopped after 34 pages of questions, the manager said.

Fourth, professionals grappling with ethical uncertainties turned to organizational structures and procedures to arrive at judgments about what to do. Some of these were clearly inadequate. But others, while still largely in development, were more helpful, such as:

The key idea that emerged from our study is this: Companies seeking to use AI ethically should not expect to discover a simple set of principles that delivers correct answers from an all-knowing, Gods-eye perspective. Instead, they should focus on the very human task of trying to make responsible decisions in a world of finite understanding and changing circumstances, even if some decisions end up being imperfect.

In the absence of explicit legal requirements, companies, like individuals, can only do their best to make themselves aware of how AI affects people and the environment and to stay abreast of public concerns and the latest research and expert ideas. They can also seek input from a large and diverse set of stakeholders and seriously engage with high-level ethical principles.

This simple idea changes the conversation in important ways. It encourages AI ethics professionals to focus their energies less on identifying and applying AI principles though they remain part of the story and more on adopting decision-making structures and processes to ensure that they consider the impacts, viewpoints and public expectations that should inform their business decisions.

Ultimately, we believe laws and regulations will need to provide substantive benchmarks for organizations to aim for. But the structures and processes of responsible decision-making are a place to start and should, over time, help to build the knowledge needed to craft protective and workable substantive legal standards.

Indeed, the emerging law and policy of AI focuses on process. New York City passed a law requiring companies to audit their AI systems for harmful bias before using these systems to make hiring decisions. Members of Congress have introduced bills that would require businesses to conduct algorithmic impact assessments before using AI for lending, employment, insurance and other such consequential decisions. These laws emphasize processes that address in advance AIs many threats.

Some of the developers of generative AI have taken a very different approach. Sam Altman, the CEO of OpenAI, initially explained that, in releasing ChatGPT to the public, the company sought to give the chatbot enough exposure to the real world that you find some of the misuse cases you wouldnt have thought of so that you can build better tools. To us, that is not responsible AI. It is treating human beings as guinea pigs in a risky experiment.

Altmans call at a May 2023 Senate hearing for government regulation of AI shows greater awareness of the problem. But we believe he goes too far in shifting to government the responsibilities that the developers of generative AI must also bear. Maintaining public trust, and avoiding harm to society, will require companies more fully to face up to their responsibilities.

Dennis Hirsch, Professor of Law and Computer Science; Director, Program on Data and Governance; core faculty TDAI, The Ohio State University and Piers Norris Turner, Associate Professor of Philosophy & PPE Coordinator; Director, Center for Ethics and Human Values, The Ohio State University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Artificial intelligence (AI) applications have created a buzz on the internet and with investors, and have the potential to transform the aviation industry. From flight data analytics to optimized route and fuel planning applications, AI, in its infancy, is making an impact on aviationat least operationally. But can it improve safety?

Natural language AI chatbots, such as ChatGPT, according to technology publication Digital Trends, continue to dazzle the internet with AI-generated content, morphing from a novel chatbot into a piece of technology that is driving the next era of innovation. In a mixed outlook, one article states, No tech product in recent memory has sparked as much interest, controversy, fear, and excitement.

First launched as a prototype in November 2022, ChatGPT (Openai.com) quickly grew to more than 100 million users by January 2023. Last month, traffic grew by more than 54 percent and is closing in on one billion unique users every month.

ChatGPT is a chatbot built on what is called a Large Language Model (LLM). According to Digital Trends, These neural networks are trained on huge quantities of information from the internet for deep learningmeaning they generate altogether new responses, rather than regurgitating specific canned responses.

While most adults in the U.S. have heard about ChatGPT, only 14 percent have used it. To learn more about what all the hype is about, I asked ChatGPT a few aviation-related questions. Its a bit tongue-in-cheek, but this was an exercise to satisfy my curiosity and to see if the responses were either accurate or innovative.

For fun, I submitted a prompt to ChatGPT with the following question: How can we improve aviation safety?

In a matter of seconds, the bot generated a tidy response with an opening statement and 10 key safety measures. It acknowledged that Aviation safety is a critical concern, and there are several ways to improve it.

Of those 10 safety measures, there were seven categories that included: enhanced training and education (pilots, maintenance personnel, and air traffic controllers), strengthening of safety regulations, implementation of advanced technology (including AI), investment in research and development (including ATC and NAS modernization), improved data sharing, comprehensive safety audits, and the fostering of a safety culture that includes improved reporting and communications.

A brief description of each safety measure was provided. Most relied on overused jargon or buzzwords such as safety culture, data sharing, and best practices. (For the college student experimenting with generative AI, here is a pro tip. Provide a little context to each term.)

In general, a lot of these are white cape safety measures that are easier to talk about than to implement. As an example, improved safety regulations often fall victim to powerful lobbying groups in Washington, D.C. A great example of a regulation that was influenced by lobbyists are the more proactive science-based Part 117 duty and rest rules that applied to all Part 121 operators, except cargo airlines.

Under implementation of advanced technologies, there was some serious self-promotion of AI by stating, Utilize automation and artificial intelligence to enhance decision-making processes, reduce human error, and provide real-time safety information.

Agreed, these are the areas where AI will shine. Recently, the Notam Alliancea team of notam end-users, pilots, dispatchers, airlines, and other operatorsran an exercise to create a super Notam that helps solve issues with the readability and useability of notams. The group used ChatGPT to see if notams could be understood by a machine; the results were promising, during this demonstration, AI could understand a notam over 98 percent of the time. (More on this in an upcoming AIN article.)

So, can AI improve aviation safety? The short answer is yes, but along the way, there will be appropriate applications of AI and it will continue to create interest, controversy, fear, and excitement. According to the ChatGPT response, Its important to note that aviation safety is an ongoing process that requires continuous improvement, vigilance, and collaboration among all stakeholders in the aviation industry.

Its also important to note that AI is not the end of humanity. For those humans with critical thinking skills, and the ability to use prior experiences to perform complex tasks (pilots, safety professionals, and writers), the future is also bright.

The opinions expressed in this column are those of the author and are not necessarily endorsed byAINMedia Group.

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New research highlights how language-generating AI models could make it easier to create dangerous germs.

Heres an important and arguably unappreciated ingredient in the glue that holds society together: Google makes it moderately difficult to learn how to commit an act of terrorism. The first several pages of results for a Google search on how to build a bomb, or how to commit a murder, or how to unleash a biological or chemical weapon, wont actually tell you much about how to do it.

Its not impossible to learn these things off the internet. People have successfully built working bombs from publicly available information. Scientists have warned others against publishing the blueprints for deadly viruses because of similar fears. But while the information is surely out there on the internet, its not straightforward to learn how to kill lots of people, thanks to a concerted effort by Google and other search engines.

How many lives does that save? Thats a hard question to answer. Its not as if we could responsibly run a controlled experiment where sometimes instructions about how to commit great atrocities are easy to look up and sometimes they arent.

But it turns out we might be irresponsibly running an uncontrolled experiment in just that, thanks to rapid advances in large language models (LLMs).

When first released, AI systems like ChatGPT were generally willing to give detailed, correct instructions about how to carry out a biological weapons attack or build a bomb. Over time, Open AI has corrected this tendency, for the most part. But a class exercise at MIT, written up in a preprint paper earlier this month and covered last week in Science, found that it was easy for groups of undergraduates without relevant background in biology to get detailed suggestions for biological weaponry out of AI systems.

In one hour, the chatbots suggested four potential pandemic pathogens, explained how they can be generated from synthetic DNA using reverse genetics, supplied the names of DNA synthesis companies unlikely to screen orders, identified detailed protocols and how to troubleshoot them, and recommended that anyone lacking the skills to perform reverse genetics engage a core facility or contract research organization, the paper, whose lead authors include MIT biorisk expert Kevin Esvelt, says.

To be clear, building bioweapons requires lots of detailed work and academic skill, and ChatGPTs instructions are probably far too incomplete to actually enable non-virologists to do it so far. But it seems worth considering: Is security through obscurity a sustainable approach to preventing mass atrocities, in a future where information may be easier to access?

In almost every respect, more access to information, detailed supportive coaching, personally tailored advice, and other benefits we expect to see from language models are great news. But when a chipper personal coach is advising users on committing acts of terror, its not so great news.

But it seems to me that you can solve the problem from two angles.

We need better controls at all the chokepoints, Jaime Yassif at the Nuclear Threat Initiative told Science. It should be harder to induce AI systems to give detailed instructions on building bioweapons. But also, many of the security flaws that the AI systems inadvertently revealed like noting that users might contact DNA synthesis companies that dont screen orders, and so would be more likely to authorize a request to synthesize a dangerous virus are fixable!

We could require all DNA synthesis companies to do screening in all cases. We could also remove papers about dangerous viruses from the training data for powerful AI systems a solution favored by Esvelt. And we could be more careful in the future about publishing papers that give detailed recipes for building deadly viruses.

The good news is that positive actors in the biotech world are beginning to take this threat seriously. Ginkgo Bioworks, a leading synthetic biology company, has partnered with US intelligence agencies to develop software that can detect engineered DNA at scale, providing investigators with the means to fingerprint an artificially generated germ. That alliance demonstrates the ways that cutting-edge technology can protect the world against the malign effects of ... cutting-edge technology.

AI and biotech both have the potential to be tremendous forces for good in the world. And managing risks from one can also help with risks from the other for example, making it harder to synthesize deadly plagues protects against some forms of AI catastrophe just like it protects against human-mediated catastrophe. The important thing is that, rather than letting detailed instructions for bioterror get online as a natural experiment, we stay proactive and ensure that printing biological weapons is hard enough that no one can trivially do it, whether ChatGPT-aided or not.

A version of this story was initially published in the Future Perfect newsletter. Sign up here to subscribe!

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What happened

Though C3.ai's (AI -10.82%) leadership team made multiple optimistic pronouncements during an investor day update Thursday evening, shares of the artificial intelligence company were trading down by 10.6% as of 12:45 p.m. ET Friday.

Citing "broad interest in AI" across industries, CFO Juho Parkinnen said the company's pipeline of new contracts in the works has "basically doubled" since the beginning of its fiscal year. At the same time, management said the "sales cycle is shortening" on these opportunities, with prospects turning into contracts at a faster rate, reports TheFly.com.

So far, so good, right? But if this is the case, then why did C3.ai stock slump Friday?

According to the CFO, at this time last fiscal year, C3.ai had successfully landed 10 deals with prospective clients, whereas so far this fiscal year, the company has landed 16 deals. Problem is, as investment bank JP Morgan points out, most of these deals are for pilot projects, and not full-fledged, long-term contracts. The bank wants to see how many of these pilot projects turn into longer contracts so it can determine how accurate "the assumptions around the consumption-based pricing model" are.

Similarly, investment bank DA Davidson wrote Friday that it's pretty sure C3.ai's success in landing pilot projects is already reflected in the stock's price -- which, after all, has roughly tripled since the start of this year.

DA Davidson isn't coming right out and saying that it thinks C3.ai stock is overpriced, mind you. However, it reiterated the $30 price target that it put on the stock early this month. Given that C3.ai is trading at around $33, that's kind of the same thing, and suggests a downgrade may be imminent -- though it did maintain its neutral rating.

Given that the company has no profits and trades at more than 15 times sales, there's a lot of hype built into C3.ai's valuation right now. Discretion may be the better part of valor on this one, folks. Until C3.ai proves that it can turn its pilot deals into long-term contracts, and its long-term contracts into sustainable profits, invest in it at your own risk.

JPMorgan Chase is an advertising partner of The Ascent, a Motley Fool company. Rich Smith has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends JPMorgan Chase. The Motley Fool recommends C3.ai. The Motley Fool has a disclosure policy.

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Why C3.ai Stock Crashed by 10% on Friday - The Motley Fool