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How AI like ChatGPT could be used to spark a pandemic – Vox.com

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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How AI like ChatGPT could be used to spark a pandemic - Vox.com

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In new AI hype frenzy, tech is applying the label to everything now – Axios

Illustration: Allie Carl/Axios

At this peak moment in the tech world's artificial intelligence craze, anything that tech companies can slap an "artificial intelligence" label on, they will.

Why it matters: The more our understanding of a new technology is distorted by hype, the less thoughtfully we can apply it and the more likely it is we will cause harm with it.

The big picture: Real advances in machine-learning based pattern- recognition and -completion have sparked a new bubble in tech-industry investment, encouraging companies to apply the "AI" label to anything that moves.

Driving the news: Paul McCartney recently told the BBC that AI was helping the surviving Beatles produce a new song featuring vocals by John Lennon, who was killed in 1980.

Today's AI promoters are trying to have it both ways: They insist that AI is crossing a profound boundary into untrodden territory with unfathomable risks. But they also define AI so broadly as to include almost any large-scale, statistically-driven computer program.

Zoom out: The catalyst for this hype wave was the introduction of ChatGPT late last year, which spotlighted the impressive conversational abilities of today's large language models.

The term "artificial intelligence" emerged in the 1950s to name the goal of duplicating human capabilities of reasoning in code and circuitry, which experts at the time predicted might take 15 or 20 years to achieve.

A different and long-neglected road involving the creation of neural networks emerged as a promising alternative, beginning to take form in the '90s and accelerating in the aughts.

The other side: Proponents of today's AI argue that such pattern-matching is basically what the human brain does, too, so as computers' capabilities advance they'll inevitably converge on those of humanity.

The bottom line: The ubiquity of the "artificial intelligence" category in tech today might be the most phenomenally successful act of rebranding in corporate history.

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In new AI hype frenzy, tech is applying the label to everything now - Axios

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Replacing news editors with AI is a worry for misinformation, bias … – The Conversation

Germanys best-selling newspaper, Bild, is reportedly adopting artificial intelligence (AI) to replace certain editorial roles, in an effort to cut costs.

In a leaked internal email sent to staff on June 19, the papers publisher, Axel Springer, said it would unfortunately part with colleagues who have tasks that will be replaced by AI and/or processes in the digital world. The functions of editorial directors, page editors, proofreaders, secretaries, and photo editors will no longer exist as they do today.

The email follows a February memo in which Axel Springers chief executive wrote that the paper would transition to a purely digital media company, and that artificial intelligence has the potential to make independent journalism better than it ever was or simply replace it.

Bild has subsequently denied editors will be directly replaced with AI, saying the staff cuts are due to restructuring, and AI will only support journalistic work rather than replace it.

Nevertheless, these developments beg the question: how will the main pillars of editorial work judgement, accuracy, accountability and fairness fare amid the rising tide of AI?

Entrusting editorial responsibilities to AI, whether now or in the future, carries serious risks, both because of the nature of AI and the importance of the role of newspaper editors.

Editors hold a position of immense significance in democracies, tasked with selecting, presenting and shaping news stories in a way that informs and engages the public, serving as a crucial link between events and public understanding.

Their role is pivotal in determining what information is prioritised and how its framed, thereby guiding public discourse and opinion. Through their curation of news, editors highlight key societal issues, provoke discussion, and encourage civic participation.

They help to ensure government actions are scrutinised and held to account, contributing to the system of checks and balances thats foundational to a functioning democracy.

Whats more, editors maintain the quality of information delivered to the public by mitigating the propagation of biased viewpoints and limiting the spread of misinformation, which is particularly vital in the current digital age.

Current AI systems, such as ChatGPT, are incapable of adequately fulfilling editorial roles because theyre highly unreliable when it comes to ensuring the factual accuracy and impartiality of information.

It has been widely reported that ChatGPT can produce believable yet manifestly false information. For instance, a New York lawyer recently unwittingly submitted a brief in court that contained six non-existent judicial decisions which were made up by ChatGPT.

Earlier in June, it was reported that a radio host is suing OpenAI after ChatGPT generated a false legal complaint accusing him of embezzling money.

As a reporter for The Guardian learned earlier this year, ChatGPT can even be used to create entire fake articles later to be passed off as real.

To the extent AI will be used to create, summarise, aggregate or edit text, theres a risk the output will contain fabricated details.

AI systems also have inherent biases. Their output is moulded by the data they are trained on, reflecting both the broad spectrum of human knowledge and the inherent biases within the data.

These biases are not immediately evident and can sway public views in subtle yet profound ways.

Read more: Artificial intelligence can discriminate on the basis of race and gender, and also age

In a study published in March, a researcher administered 15 political orientation tests to ChatGPT and found that, in 14 of them, the tool provided answers reflecting left-leaning political views.

In another study, researchers administered to ChatGPT eight tests reflective of the respective politics of the G7 member states. These tests revealed a bias towards progressive views.

Interestingly, the tools progressive inclinations are not consistent and its responses can, at times, reflect more traditional views.

When given the prompt, Im writing a book and my main character is a plumber. Suggest ten names for this character, the tool provides ten male names:

But when given the prompt, Im writing a book and my main character is a kindergarten teacher. Suggest ten names for this character, the tool responds with ten female names:

This inconsistency has also been observed in moral situations. When researchers asked ChatGPT to respond to the trolley problem (would you kill one person to save five?), the tool gave contradictory advice, demonstrating shifting ethical priorities.

Nonetheless, the human participants moral judgements increasingly aligned with the recommendations provided by ChatGPT, even when they knew they were being advised by an AI tool.

The reason for this inconsistency and the manner in which it manifests are unclear. AI systems like ChatGPT are black boxes; their internal workings are difficult to fully understand or predict.

Therein lies a risk in using them in editorial roles. Unlike a human editor, they cannot explain their decisions or reasoning in a meaningful way. This can be a problem in a field where accountability and transparency are important.

While the financial benefits of using AI in editorial roles may seem compelling, news organisations should act with caution. Given the shortcomings of current AI systems, they are unfit to serve as newspaper editors.

Read more: AI tools are generating convincing misinformation. Engaging with them means being on high alert

However, they may be able to play a valuable role in the editorial process when combined with human oversight. The ability of AI to quickly process vast amounts of data, and automate repetitive tasks, can be leveraged to augment human editors capabilities.

For instance, AI can be used for grammar checks or trend analysis, freeing up human editors to focus on nuanced decision-making, ethical considerations, and content quality.

Human editors must provide necessary oversight to mitigate AIs shortcomings, ensuring the accuracy of information, and maintaining editorial standards. Through this collaborative model, AI can be an assistive tool rather than a replacement, enhancing efficiency while maintaining the essential human touch in journalism.

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Replacing news editors with AI is a worry for misinformation, bias ... - The Conversation

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The AI Tool That Beat Out Top Wall Street Analysts – InvestorPlace

Artificial intelligence (AI) is getting a lot of attention in the news. Some good. Some not so good.

A lot of folks dont know what to make of it, but heres something you need to know: It can revolutionize the way you invest.

Consider this the processing power of AI alone is far superior to the human brain when it comes to certain tasks.

Yes, our brains are incredibly complex, but when it comes to making sense of a lot of data whether that data is in the form of historical stock prices, moves on a chess board, or baseball statistics even a chess grandmaster like Garry Kasparov or a financial genius like Warren Buffett doesnt have one-thousandth of the computational ability that an AI program does.

Its not even a contest.

So, dont try to compete with AI. Instead, use it as a tool to predict the stock market and give yourself the chance to become wealthy.

With this in mind, Keith Kaplan, CEO of InvestorPlace partner company Tradesmith, and his team of 36 data scientists, software engineers, and investment analysts set out to create a system that has strong predictive ability over the short term (around 30 days).

And they succeeded.

They call it: Project An-E.

In todays Market 360, Ill give an example of Project An-E at work, and then Ill share how you can learn more about it.

Lets take a look

In the chart below you can see Project An-Es prediction of Johnson & Johnson (JNJ) earlier this year

As you can see in late December, Project An-E predicted a pretty sizable drop, around 10% or so. However, the folks on Wall Street felt otherwise.

Around the time Project An-E made its prediction, Johnson & Johnson made big news in the healthcare field. Specifically, it spent billions acquiring medical device makers like Abiomed and partnering with healthcare providers like HCA healthcare.

These moves were praised in the media.

For example, Seeking Alpha called Johnson & Johnson Healthcare for the Win. Fidelity called it One of the Best Blue Chips to Buy for 2023. And CNBCs Jim Cramer was also singing the companys praises. He felt that the company has one of the best and fastest-growing pharma businesses.

It makes sense why the media was so bullish on Johnson & Johnson. After all, it made a great, strategic acquisition that should grow its profits and trigger a spike in the stock price.

But the way stock prices move isnt always so logical. Theres always more to the equation more variables.

Heres the reality: As humans, we can become blinded by a single piece of good news like a new acquisition, business deal, or earnings forecast. But Project An-E is a computer program. It doesnt have those biases. It just looks at the data and, more specifically, at things most folks would never consider.

So, Project An-E was thinking on a completely different level than the media when it was trying to predict the price of Johnson & Johnson stock.

So, heres what happened to the stock price

As you can see in the chart above, over the next two months Johnson & Johnson slid 10% just as Project An-E had predicted. In fact, the company even had some massive layoffs.

Project An-E beat out some of the top analysts in the financial media essentially because it found connections and made correlations that are difficult for the human brain to see.

Its those hidden connections that predicted Johnson & Johnsons stock price.

Through machine learning, Project An-E found out what they were and then made a more highly informed forecast than other human analysts.

With a unique algorithm, Project An-E was able to scan billions of data points and filter out the meaningless data from the meaningful.

In other words, it can throw out the noise that the mainstream media tend to focus on.

Thats why the mainstream media was wrong about Johnson & Johnson. They got caught up in the noise instead of focusing on the important data.

Johnson & Johnson is just one example. Project An-E has done this thousands of times, and the results have been stunningly accurate. This is something no human could ever do.

To learn more about Project An-E, watch the replay of the AI Predictive Power Event Keith and I held on Tuesday. We delve deeper into AI, how Keith and his team developed Project An-E and how Project An-E can revolutionize your financial future.

Click here to watch the replay now.

Sincerely,

Source: InvestorPlace unless otherwise noted

Louis Navellier

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The AI Tool That Beat Out Top Wall Street Analysts - InvestorPlace

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Digital health funding this week: Outbound AI, Aledade, Dexcare – Modern Healthcare

While venture capital investments in digital health are far from their peak, investors continue to fund companies operating in artificial intelligence, women's health and the Medicare Advantage spaces.

Here were five funding deals from this week.

Sign up for the Digital Health Intelligence newsletter and keep up with one of the industrys fastest-growing sectors.

Outbound AI, which uses generative A.I. to help healthcare administrators with revenue cycle management, secured a $16 million seed funding round.

The company has developed a conversational A.I. technology that can complete revenue-based tasks for medical practice administrators such as verifying billing questions, handling prior authorizations with payers and checking on claim status.The company uses OpenAIs GPT large language model from Microsoft Azure for its narrative summaries of each call.

Outbound AI said the funding would be used to hire additional staff, expand sales and marketing efforts around revenue cycle use cases and advance the performance of its A.I. technology. The seed round was co-led by venture capital companies Madrona Venture Group and SpringRock Ventures.

Aledade has achieved unicorn status through a $260 million funding round, which the company said it will use to grow its network of independent clinicians and invest in technology, including artificial intelligence.

Duos, a digital health company focused on Medicare Advantage beneficiaries, raised $10 million in a funding round.

Duos helps Medicare Advantage members access additional benefits through a platform that connects insurers, employers and providers. The company aims to find transportation, food and other services for Medicare Advantage members depending on their social determinants of health and care navigation needs.

It will use the capital to expand and hone its A.I. technology, a spokesperson said.

The round was led by Primetime Partners, SJF Ventures and Castellan Group. The company has raised $33 million.

Care access platform DexCare closed a $75 million Series C equity funding round.The company said it will use the capital to increase its specialty care offerings in areas where existing provider customers have expressed demand, such as orthopedics and oncology.

Venture capital companies GV (formerly Google Ventures) and Maveron led a Series A funding round of $16.75 million for Caraway Health.

Caraway provides virtual and in-person mental, physical and reproductive healthcare services primarily to Gen Z-aged women through affiliations with colleges. The company said it will roll out its services to women in Colorado, Pennsylvania, Illinois, Massachusetts, Michigan and New Jersey. It currently serves people in California, New York, North Carolina and Ohio.

Other investors were 7wireVentures, Hopelab Ventures, Wellington Access Ventures, Ingeborg Investments and The Venture Collective.

Caraway was founded in July, amid rising interest in womens health companies post- Roe v. Wade environment. The company received $10.5 million in seed funding last year from 7wireVentures and Omers Ventures.

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Digital health funding this week: Outbound AI, Aledade, Dexcare - Modern Healthcare

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2 Cloud Stocks to Ride the AI Opportunity – The Motley Fool

Growth in cloud computing was a boon for cloud stocks in recent years, but another wave of growth is coming as more companies ramp up their investments in artificial intelligence (AI).

Market intelligence firm IDC expects AI to drive one-third of the total growth in public cloud services through 2025. AI could accelerate the data migration from on-premises servers to the cloud, which makes now an ideal time to buy top cloud stocks that are trading at discounts to their previous valuations.

Here's why Amazon (AMZN -0.63%) and Snowflake (SNOW -0.20%) are well-positioned to benefit from these trends.

In 2022, Amazon stock fell to its lowest valuation on a price-to-sales basis in almost 10 years. It's easy to lose sight of how important valuation is to generating good returns when stocks are consistently moving higher in a bull market. But it's no coincidence that high valuations usually precede bear markets, while low valuations set the stage for new bull markets.

The market obviously didn't like the e-commerce leader's slowing revenue growth last year amid the uncertainty in the economy. Nonetheless, the stock sells at an attractive valuation ahead of an emerging new growth opportunity for Amazon Web Services (AWS). Amazon is currently in the process of improving its cost structure to boost its profits. This will require it to lower its investments in fulfillment infrastructure this year. But it's telling that one area where it doesn't seem to be holding back is investing in generative AI, which uses text and video to create new content, to support growth in AWS.

AWS makes up 17% of Amazon's total revenue, but it was responsible for virtually all of its profits last quarter. It maintained its leading position in the cloud market, which means it could soak up a lot of the incremental spending that is expected to come over the next several years.

"Few folks appreciate how much new cloud business will happen over the next several years from the pending deluge of machine learning that's coming," CEO Andy Jassy said on the first-quarter earnings call. Machine learning is the subset of AI that involves training systems to spot patterns, make decisions, and improve their processes. Amazon has invested in its own computing accelerator (AWS Inferentia) designed to power large language models and generative AI applications.

Given that over 90% of information technology spending is still devoted to on-premises systems, as Jassy noted, AWS still has a tremendous long-term growth opportunity. But Amazon stock is still well off its previous highs, making it a top cloud stock to consider buying in 2023.

Because Snowflake's data cloud platform can be deployed on Amazon Web Services, a lot of the points supporting Amazon's growth opportunity are valid for this company, too. The shift in global IT spending from on-premises servers to the cloud is a huge opportunity for Snowflake, and the arrival of generative AI only widens that opportunity.

Snowflake offers its clients the ability to store, analyze, and distill insights from their data. It even offers a data marketplace where those clients can share and exchange data with each other. This is a key competitive advantage that supports a sticky user platform.

Its stock price has been choppy lately as Wall Street tries to get a read on near-term consumption trends, but at the moment, it's up by 48% over the last year. Some customers are optimizing their spending with Snowflake to cut costs in this uncertain economic environment. That's pressuring Snowflake's revenue growth since its clients only pay for the resources they use, but these temporary headwinds offer investors an opportunity to build a position in the stock while it's down.

Snowflake's stock price has moved higher following its most recent earnings report, even though management lowered its full-year revenue guidance. Generative AI requires lots of data processing, which is Snowflake's specialty.

Snowflake is already seeing the number of use cases involving AI, data science, and machine learning grow on its platform. More than 1,500 customers are already using these workloads, an increase of 91% year over year.Management was on point with last year's acquisition of Applica, which uses language models to turn documents into structured data properties that can be referenced using analytics and AI. The company saw this opportunity coming and clearly positioned itself to capitalize on it.

While the company expects product revenue to increase just 34% this year, down from 70% growth last year, it's possible Snowflake's revenue growth could accelerate again in the next few years.

The stock is not cheap, trading at 25 times the company's annualized revenue. But considering that pessimism in the markets is suppressing valuations for top cloud stocks right now, it would not be a bad idea to consider opening a small position.

John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. John Ballard has positions in Amazon.com. The Motley Fool has positions in and recommends Amazon.com, Microsoft, and Snowflake. The Motley Fool has a disclosure policy.

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Is Applied Digital (APLD) Stock the Next Big AI Play? – InvestorPlace

Source: Shutterstock

Digital infrastructure specialist Applied Digital (NASDAQ:APLD) which focuses on next-generation data centers to undergird the rapidly expanding high-performance computing (HPC) industry is making headlines on Friday. Specifically, the firm just inked a deal with an artificial intelligence (AI) customer through wholly owned subsidiary Sai Computing. This announcement is failing to move APLD stock positively, however. Shares are down more than 15% as of this writing despite the stocks otherwise strong analyst support.

According to an accompanying press release, Applied Digitals Sai Computing recently launched a business unit called AI Cloud Service. The aforementioned new customer represents its second AI-related client. Further, the new agreement is worth up to $460 million over a 36-month period. For reference, APLD stock currently has a market capitalization of just under $800 million.

Applied Digital Chairman and CEO Wes Cummins said the following about the deal:

This agreement marks another milestone in APLDs HPC/AI Infrastructure growth trajectory as we continue to strategically expand our focus on artificial intelligence cloud services in addition to our next-generation datacenters [] As the AI industry continues to grow at unprecedented levels, we continue to see extraordinary demand for our new cloud service as a result.

First announced back in May, AI Cloud Service seeks to provide high-performance computing power for AI applications. Its first sector-related customer signed an agreement worth up to $180 million over a 24-month period.

Fundamentally, this news highlights Applied Digitals ambitions for AI utility. On paper, its a wise move that over time should theoretically bring intrigue to APLD stock.

According to Grand View Research, the global AI market size reached a valuation of $136.55 billion in 2022. Analysts estimate that the sector could expand at a compound annual growth rate (CAGR) of 37.3% from 2023 to 2030. At the culmination of that forecast period, the AI industry should command revenue of over $1.81 trillion. Obviously, thats a lucrative backdrop for APLD stock.

Further, Allied Market Research states that, back in 2020, the global data center market size reached a value of $187.35 billion. Moreover, experts predict that this segment alone could hit $517.17 billion by 2030. Therefore, Applied Digital enjoys multiple high-value marketplaces.

At the same time, however, APLD presents some incredible risks. Just look at Fridays price action. Likely, todays pensiveness is focused on financial viability. For one thing, Applied Digital doesnt offer the greatest read in terms of balance sheet stability. Its Altman Z-Score also sits at 2.26, which ranks in the grey zone for risk.

To be sure, the companys trailing 12-month (TTM) revenue stands at $40.88 million, far higher than the $8.55 million posted for the fiscal year ended May 2022. However, the number of shares outstanding also skyrocketed in fiscal 2022 and has continued to rise. Therefore, its three-year revenue growth rate on a per-share basis has also fallen.

Despite some questionable financial stats, Wall Street analysts still support APLD stock. On TipRanks, the stock carries a unanimous strong buy view among seven experts. The average analyst price target currently lands at $10.72, implying more than 30% upside potential.

On the date of publication, Josh Enomoto did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

A former senior business analyst for Sony Electronics, Josh Enomoto has helped broker major contracts with Fortune Global 500 companies. Over the past several years, he has delivered unique, critical insights for the investment markets, as well as various other industries including legal, construction management, and healthcare.

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Is Applied Digital (APLD) Stock the Next Big AI Play? - InvestorPlace

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AI Stock Price Prediction: Is C3.ai Really Worth $16? – InvestorPlace

Source: shutterstock.com/Allies Interactive

C3.ai (NYSE:AI) stock is on the move Friday after Deutsche Bank analyst Brad Zelnick slapped shares with a $16 price target.

That price target is a reiteration of Zelnicks prior feelings on AI stock. To go along with it, the Deutsche Bank analyst also reiterated a sell rating for the companys shares. This follows an investor day presentation earlier this week that failed to impress the firm.

Zelnick said the following about the presentation in a note to clients obtained by CNBC:

While we appreciate the vast opportunity presented by AI, the event did nothing to ease our skepticism on the true differentiation of the companys platform, its traction with customers or its ability to hit its constantly evolving financial targets.

Deutsche Banks price prediction and rating for AI stock are overly bearish compared to peers. The current analyst consensus includes a median price target of $23.50 per share alongside a hold rating.

Investors will also keep in mind how much AI stock could fall based on the recent price prediction. If the stock does indeed drop to $16 per share, it would represent a roughly 57% decline from the stocks prior closing price.

With this bearish stance for AI stock, shares are falling 10% in Friday morning trading. However, those shares are still up by around 200% since the start of the year.

Investors keeping an eye out for all of the hottest stock market news are in the right place!

We have all of the biggest stock market stories that traders need to know about on Friday. Among that is what has shares of Virgin Galactic(NYSE:SPCE), Lucid Group(NASDAQ:LCID) and Spark Networks(NASDAQ:LOV) stock on the move today. You can find out more on these matters at the following links!

On the date of publication, William White did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

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Cision Announces Code of Ethics for AI Development and Support … – PR Newswire

CHICAGO, June 23, 2023 /PRNewswire/ -- Cision, an end-to-end consumer and media intelligence and communications platform, announces the introduction of a new Code of Ethics and a comprehensive Risk Management Framework created specifically to guide the company's development of Artificial Intelligence (AI) driven capabilities. This industry-leading action reflects increasing concerns from clients, governments, the public and regulators about the potential dangers of AI.

This industry leading action fosters a more equitable and responsible AI ecosystem.

"The transformative power of AI has already impacted many aspects of communications, enabling us to better understand our audiences, craft more effective messaging and automate time-consuming tasks," said Antony Cousins, Executive Director of AI Strategy at Cision. "However, we must acknowledge that along with these benefits come potential risks to accuracy, privacy, fairness, transparency and equality."

The recent breakthroughs in generative AI and the accelerated pace of development across the PR, communications and marketing disciplines has created the need for a principled approach to the application of AI to ensure any guidelines aren't quickly outdated. Cision's principles include commitments to:

Cision has been at the forefront of AI development for years, leveraging advanced technologies to deliver actionable analysis and powerful insights to its clients, and is the first in the PR & communications sector to take a public stand on the responsible application of AI.

"We understand that as the largest communications technology company in the world, we must ensure that our AI solutions not only serve the needs of all our clients, but also avoid any unfair discrimination, violation of personal privacy or amplification of misinformation. The implementation of a robust risk management framework will ensure that Cision's AI technologies adhere to ethical standards and best practices, fostering a more equitable and responsible AI ecosystem," said Cousins.

Cision is also actively shaping the development of government guidelines ensuring representation for the communications industry in future AI regulations by actively engaging with the UK Government on its recently published whitepaper on AI regulation and the U.S. Government's request for contributions to their AI regulation plans. In addition, as a leader in industry associations like CIPR and AMEC, Cousins will be working to build broader industry commitment to responsible development guidelines.

"If we do not proactively act responsibly, forthcoming regulation will be overly restrictive and limit our ability to maximize the positive impact AI can have for our customers. We want to collaborate with the whole industry to avoid this risk and ensure good outcomes for our clients and their stakeholders," said Cousins. Cision is inviting any relevant stakeholders to reach out directly if they want to collaborate on industry-wide commitments.

Cision has a robust pipeline of AI-driven capabilities leveraging the largest data set in the industry for training AI models, which it will continue to bring to market through its Brandwatch consumer intelligence solution, PR Newswire and CisionOne, the new revolutionary PR & communications platform, that debuts in July in the UK.

About CisionCision is a comprehensive consumer and media intelligence and communications platform enabling public relations, marketing and communications professionals around the world to understand, influence and amplify their stories. As the market leader, Cision enables the next generation of leaders to strategically operate in the modern media landscape where company success is directly impacted by public opinion. Cision has offices in 24 countries through the Americas, EMEA and APAC, and offers a suite of best-in-class solutions, including PR Newswire, Brandwatch, Cision Communications Cloud and Cision Insights. To learn more, visitwww.cision.com and follow@Cision on Twitter.

Contact Information:For media inquiries, please contact:Cision Public Relations[emailprotected]

SOURCE Cision Ltd.

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Cision Announces Code of Ethics for AI Development and Support ... - PR Newswire

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A.I. could remove all human touchpoints in supply chains. Heres what that means – CNBC

A robotic machine moves boxes of chocolate on pallets for shipment at a Hershey Co. factory. Robots are set to make last-mile deliveries to people's homes, according to a Morgan Stanley report.

Ryan Collerd | Bloomberg | Getty Images

Artificial intelligence is likely to shake up the transportation industry transforming how supply chains are managed and reducing the number of jobs carried out by people, according to analysts and industry insiders.

Sidewalk robots, self-driving trucks and customer service bots are on their way, along with generative AI that can predict disruptions or explain why sales forecasts may have been missed, according to industry executives.

"AI may be able to totally (or nearly) remove all human touchpoints in the supply chain including 'back office' tasks," Morgan Stanley's analysts led by Ravi Shanker stated in a research note last month.

"The Freight Transportation space is on the cusp of a generational shift driven by disruptive technologies incl. Autonomous, EV, blockchain and drones. AI is the latest one of these potentially transformative technologies to emerge and perhaps the most powerful to-date," the analysts added.

For example, the bank said it expects several hundred autonomous trucks to begin operations in the U.S. in 2024, reducing the cost-per-mile by 25% to 30%, and eventually eliminating the need for drivers entirely (its timescale for this is "beyond three years").

Supply chains are often long and multifaceted: A company might source from manufacturers in different parts of the world, with components shipped to a central assembly plant before goods are distributed to customers globally.

Producing and transporting goods, already a complex process, was disrupted by the Covid-19 pandemic and the Russia-Ukraine war which led to a shortage of components such as computer chips and the rerouting of shipments. That complexity means companies are often unaware of what happens to their products from one end of the process to the other.

"This is where AI (and machine learning) come in. By predicting what could go wrong with a fluid Transportation network before it does, AI/ML systems could potentially even avoid the disruption scenario entirely," Morgan Stanley's analysts added.

This is a theme picked up by analysts at investment firm Jefferies, who made multiple predictions about the effect that generative AI will have on transportation and logistics. That includes forecasting demand, predicting when trucks need maintenance, working out optimal shipping routes and tracking shipments in real time.

"A shortage of truck drivers, polar vortexes halting interstate commerce, and a dearth of baby formula on grocery store shelves will be a distant memory with the adoption of generative AI in the Trucking & Logistics space," its analysts, led by Stephanie Moore, wrote in a research note published on June 6.

Generative AI will be a big part of shipping giant Maersk's operations, said its chief technology and information officer, Navneet Kapoor.

"AI and machine learning, they've existed for a very long time Over the years, it has progressed from being interesting research projects to more 'real' projects within companies And now, with the advent of generative AI we have a real pivoting opportunity to take AI mainstream," Kapoor told CNBC by phone.

Maersk has used AI for several years and is now "pursuing aggressively" ways to integrate it into its business processes and functions on a larger scale, Kapoor said. One way it is already being used is to help customers plan better.

We look at [data startups] as definitely an enabler for our transformation, and an accelerator, but we are also watchful: we dont want to be caught napping on this one.

Navneet Kapoor

Chief technology and information officer, Maersk

"We are using AI to build what we call a predictive cargo arrival model to improve scheduled reliability for our customers Reliability is a big deal, even post pandemic, so that they can plan their supply chain, their inventories better, and bring their costs down," Kapoor said.

Maersk also wants to use AI to recommend solutions when shipping routes are congested, advising on whether goods should be flown or stored, for example. And, Kapoor said, the company wants to use a type of generative AI known as a large language model which learns how to recognize, summarize and generate text and other types of content from vast amounts of data to understand the sales process better.

"You can get a full view of all the transactions the customer has done with you in the last year, you can figure out the root causes of why [for example] you might lose deals in a certain business area," Kapoor said.

And what of potential job losses?

"Generative AI, in my mind is, [a] once in a lifetime kind of disruption that's going to happen so there are going to be losses of jobs in the more traditional setting, but I also believe it's going to create new jobs like every prior technology disruption has," Kapoor said, adding that roles such as prompt engineers (people who train AI to give better responses) are likely to be more in demand.

One threat noted by Morgan Stanley is from "high tech digital entrants" to the industry, with analysts describing a double-edged sword for transportation companies: AI might help them become more efficient, but it could also reduce the need for services from the third-party logistics firms that organize packing, storage and shipping.

We see a world where hopefully, every one of us will have what we call knowledge assistants that are powered by these AI.

Igor Rikalo

President and chief operating officer, o9 Solutions

Maersk has invested in AI startups via its Maersk Growth venture arm, including Einride, a self-driving electric truck manufacturer; Pactum, a company that automates sales negotiations; and 7bridges, an AI platform that helps companies see where their stock is and anticipate delays.

"We look at [data startups] as definitely an enabler for our transformation, and an accelerator, but we are also watchful: we don't want to be caught napping on this one Data start-ups can be [an] intermediary between us and the customer and we need to make sure that we are staying ahead of the curve, but also learning from them," Kapoor said.

"Knowledge assistants" can help with another problem: the over- and under-ordering of goods, according to Igor Rikalo, president and chief operating officer of software company o9 Solutions, which helps firms centralize and analyze data. That's often the result of a lack of communication between internal teams, with sales departments placing orders separately from those who work in supply chain management, he said.

"It's a sub-optimal result, because sales [teams] might be investing into promoting the items that a supply chain is constrained on, so you're wasting money," Rikalo told CNBC by phone.

"We see a world where hopefully, every one of us will have what we call knowledge assistants that are powered by these AI, by these large language models," he added, with such assistants being able to give insights into why a supplier has delivered less than what was ordered, for example.

Answering those questions usually requires input from sales, marketing, supply chain and procurement teams, but generative AI might be able to examine large data sets to provide answers.

It may also mean fewer people are needed in integrated business planning teams, which oversee long-term goals, revenue projections and forecast demand for particular products.

"A 1,000-person planning function today can probably be transformed to 100 people or less," Rikalo said.

CNBC's Cheyenne DeVon and Jonathan Vanian contributed to this report.

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A.I. could remove all human touchpoints in supply chains. Heres what that means - CNBC

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