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In January, during a Democratic primary, thousands of voters received a robocall that used artificial intelligence to impersonate President Biden discouraging them from voting.

The political consultant responsible is now facing millions in fines and jail time for the 13 felony counts of voter suppression and 13 counts of impersonating a candidate, a misdemeanor.

To combat this new threat of AI deep fakes and misinformation in US elections, a new Artificial Intelligence Task Force is bringing together state and local elected officials to focus on ways to combat malicious AI-generated activity that threaten the democratic process.

Arizona Secretary of State Adrian Fontes doesnt speak German, but he created a deepfake that makes it nearly impossible to tell that it isnt actually Fontes speakingall to demonstrate just how alarmingly lifelike and manipulative AI-generated content can be.

Fontesalong with Michigan Secretary of State Jocelyn Benson and Minnesota Secretary of State Steve Simonare leading the fight to prepare election workers and voters in their states to be vigilante and savvy against the AI threats.

They are part of a coalition of secretaries of state working with the task force, created by the NewDEAL Forum, to develop tools and best practices to combat AI disinformation this election season.

In Michigan, weve enacted legislation to make it a crime for someone to knowingly distribute materially-deceptive deep fakes that are generated by AI when there is an intent behind it of harming the reputation of or the electoral prospects of a candidate, Secretary of State Benson told Democracy Docket, a digital news platform founded by attorney Marc Elias dedicated to voting rights and elections in the courts.

The new law, passed in November, makes that crime a felony.

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In addition to that, we require any political advertisements that are generated in whole or substantially with the use of AI to include a statement that that ad was generated by artificial intelligence. That disclaimer requirement helps equip citizens with the knowledge of how to be critical consumers.

Both the important swing states of Arizona and Michigan have developed tabletop exercises to train election clerks to identify AI, and to practice linking them with law enforcement and first Responders, both for security and to rapidly respond to issues that may occur around voting, on or before election dayand also to be prepared to stop the negative impact of AI from spreading. (See their interviews in the video below)

A NewDEAL Forum poll conducted in Arizona in April found that only 41% of respondents knew anything about AI and elections.

Generative AI presents both tremendous opportunities and significant challenges, said New York State Assemblymember Alex Bores, Co-Chair of the NewDEAL Forum AI Task Force, and one of the few state legislators with a computer science background. Our goal is to craft policies to harness AIs potential to improve public services while proactively preparing for the threats and unforeseen challenges it poses to our democratic institutions.

In March, they published a report that outlines best practices for election officialsfrom secretaries of state to county election workersto mitigate the negative impacts of AI in elections. The advice includes more short-term practices, like public information campaigns about the threats, and protocols for a rapid response when they do arise.

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The document also suggests legislation that state politicians can pass to help protect democracy from AI threats.

According to Democracy Docket, at least 40 states are introducing legislation to regulate the use of AI, but only 18 have laws that specifically address election-related AIand thankfully, now Michigan is one of them.

WATCH a discussion with Fontes and Benson on Democracy Docket (Subscribe to stay up to date with court cases around the US involving elections.)

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New AI Task Force Led By Michigan and Arizona Combats Deep Fakes and Election Misinformation in US - Good News Network

OpenAI is announcing its much-anticipated entry into the search market, SearchGPT, an AI-powered search engine with real-time access to information across the internet.

The search engine starts with a large textbox that asks the user What are you looking for? But rather than returning a plain list of links, SearchGPT tries to organize and make sense of them. In one example from OpenAI, the search engine summarizes its findings on music festivals and then presents short descriptions of the events followed by an attribution link.

In another example, it explains when to plant tomatoes before breaking down different varieties of the plant. After the results appear, you can ask follow-up questions or click the sidebar to open other relevant links. Theres also a feature called visual answers, but OpenAI didnt get back to The Verge before publication on exactly how this works.

SearchGPT is just a prototype for now. The service is powered by the GPT-4 family of models and will only be accessible to 10,000 test users at launch, OpenAI spokesperson Kayla Wood tells The Verge. Wood says that OpenAI is working with third-party partners and using direct content feeds to build its search results. The goal is to eventually integrate the search features directly into ChatGPT.

Its the start of what could become a meaningful threat to Google, which has rushed to bake in AI features across its search engine, fearing that users will flock to competing products that offer the tools first. It also puts OpenAI in more direct competition with the startup Perplexity, which bills itself as an AI answer engine. Perplexity has recently come under criticism for an AI summaries feature that publishers claimed was directly ripping off their work.

OpenAI seems to have taken note of the blowback and says its taking a markedly different approach. In a blog post, the company emphasized that SearchGPT was developed in collaboration with various news partners, which include organizations like the owners of The Wall Street Journal, The Associated Press, and Vox Media, the parent company of The Verge. News partners gave valuable feedback, and we continue to seek their input, Wood says.

Publishers will have a way to manage how they appear in OpenAI search features, the company writes. They can opt out of having their content used to train OpenAIs models and still be surfaced in search.

Responses have clear, in-line, named attribution and links

SearchGPT is designed to help users connect with publishers by prominently citing and linking to them in searches, according to OpenAIs blog post. Responses have clear, in-line, named attribution and links so users know where information is coming from and can quickly engage with even more results in a sidebar with source links.

Releasing its search engine as a prototype helps OpenAI in a few different ways. First, if SearchGPTs results are wildly incorrect like when Google rolled out AI Overviews and told us to put glue on our pizza its easier to say, well, its a prototype! Theres also potential for getting attributions wrong or maybe wholesale ripping off articles like Perplexity was accused of doing.

This new product has been whispered about for months now, with The Information reporting about its development in February, then Bloomberg reporting more in May. We reported at the same time that OpenAI had been aggressively trying to poach Google employees for a search team. Some X users also noticed a new website OpenAI has been working on that hinted toward the move.

OpenAI has slowly been bringing ChatGPT more in touch with the real-time web. When GPT-3.5 was released, the AI model was already months out of date. Last September, OpenAI released a way for ChatGPT to browse the internet, called Browse with Bing, but it appears a lot more rudimentary than SearchGPT.

The rapid advancements by OpenAI have won ChatGPT millions of users, but the companys costs are adding up. The Information reported this week that OpenAIs AI training and inference costs could reach $7 billion this year, with the millions of users on the free version of ChatGPT only further driving up compute costs. SearchGPT will be free during its initial launch, and since the feature appears to have no ads right now, its clear the company will have to figure out monetization soon.

Read more here:

OpenAI announces SearchGPT, its AI-powered search engine - The Verge

The excitement surrounding artificial intelligence (AI) has been a major driver of the stock market rally this year, with the market value of generative AI estimated to be around $1 trillion.

AIs potential to enhance productivity and transform industries such as marketing, healthcare, and manufacturing has fueled a surge in demand for leading tech companies. This surge has significantly boosted the stock prices of firms specializing in semiconductors and servers.

In this context, Finbold analyzed the ongoing trends and tracked down the two best investments in the AI sector for the upcoming month.

As companies increasingly digitize their operations to enhance efficiency, ServiceNows (NYSE: NOW) AI and machine learning-driven workflow solutions have positioned it as a market leader.

The companys financial performance in Q2 was impressive, with subscription revenues rising by 23% on a currency-adjusted basis to $2.54 billion.

ServiceNow secured 88 high-value deals, each worth over $1 million in annual contract value, marking a 26% year-over-year increase, demonstrating strong demand among large enterprises.

Investors are particularly excited about ServiceNows integration of generative AI (GenAI), as the company aims to revolutionize workflows across industries with GenAI at its core.

Looking ahead, ServiceNows forward guidance is equally promising. The company expects Q3 sales to range between $2.66 billion and $2.665 billion, indicating a robust 20.25% annual growth at the midpoint.

Additionally, ServiceNow anticipates an operating income margin of 29.5%, reflecting strong operational efficiency. Valuation metrics also show the companys potential, with a market cap of $169.66 billion and an enterprise value of $166.50 billion.

Despite high trailing and forward PE ratios of 149.93 and 55.85 respectively, the companys PEG ratio of 2.89 suggests reasonable valuation given its growth prospects.

Analyst sentiment further supports the bullish outlook, with an average 12-month price target of $870.04, representing a 5.13% upside from the current price of $827.61.

High institutional ownership, at 90.72%, also highlights strong professional investor confidence, giving it a strong buy rating.

Micron Technology (NASDAQ: MU) has experienced a remarkable 30% stock surge this year, driven by soaring demand for its high-performance memory modules from AI servers.

As a key supplier of DRAM and NAND memory for PCs and data centers, Microns AI-tailored high-bandwidth memory chips have sold out for 2024, prompting the company to raise prices.

These price increases have significantly improved its gross margin, which rose to 27% in Q3 from 19% in Q2, with an expected rise to 34% in Q4.

This AI-fueled demand also led to an 82% year-over-year revenue increase to $6.8 billion in Q3, with projections of $7.6 billion for Q4, marking a 90% increase from the previous year.

Analysts have responded positively, with KeyCorp, Needham & Company, JPMorgan Chase (NYSE: JPM), and Barclays all raising their price targets to as high as $180 and maintaining buy or overweight ratings.

Valuation metrics include a forward PE ratio of 19.65, a PS ratio of 5.68, and a PEG ratio of 1.12, indicating robust growth potential relative to earnings.

Analyst sentiment further supports the bullish outlook, with an average 12-month price of $169.08, a high forecast of $225, and a low forecast of $140.The average price target shows a 54.54% change from the last price of $109.41.

Institutional ownership stands at 84.58%, reflecting strong confidence from professional investors, giving it a strong buy rating.

Investors looking to capitalize on the AI boom should consider these stocks, as they offer promising returns in the evolving technological landscape.

Disclaimer: The content on this site should not be considered investment advice. Investing is speculative. When investing, your capital is at risk.

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Buy alert: 2 AI stocks with strong buy ratings for August 2024 - Finbold - Finance in Bold

Copilot+ PCs are about to enter their confusing era with the Asus Zenbook S 16, as we welcome x86 to the party bringing its own set of pros and cons that Ill go into.

To talk about whats going on here, the first of these new laptops packing Snapdragon X Elite rely on Arm processing. This is a mobile-first chip architecture that breaks down complex tasks into the barebones instructions and completes a single one with every tick of that processors clock cycle (otherwise known as Reduced Instruction Set Computing (RISC).

And with this choice, you get key benefits that are important to laptop users like striking that fine balance between maximizing power and giving you a nice, long battery life (as can be seen in the battery behemoth that is the HP Omnibook X).

Meanwhile, at Computex 2024, we learnt more about what Intel and AMD is bringing to the mix in terms of its x86 chipsets the architecture used for Windows PCs for over 30 years that uses Complex Instruction Set Computing (CISC) to tackle every part of a task equally. Its done the job well over those three decades, but has historically been the source behind a lot of Windows laptop battery life woes.

It made me nervous-excited in the build up to actually being able to test a laptop with the AMD Ryzen AI 9 HX 370 (what a name), and some of my nervousness has been proven right. While the Zenbook S16 is certainly improved in the battery life department when compared with other x86 laptops, it still falls behind Arm-based systems. Not only that, but we uncovered slower general performance and disk loading speeds too.

Which leaves me feeling really conflicted, because I do like the Zenbook S16 its OLED display is a feast for the eyes, keyboard and touchpad ergonomics are on point, it is very graphically capable, you wont run into any Arm-based app compatibility issues, and I absolutely adore the ceraluminum finish and premium aesthetic.

However, while it does indeed outperform the M3 MacBook Air, the nitty gritty of the results give off the vibe that this laptop is being slightly held back by the past. It makes you really think about whether it's time for Microsoft to fully turn into Arm processing for itself and other laptop makers. And I know that sort of incendiary statement brings a tonne of work for developers turning their apps and games to Arm or relying on the Prism emulation layer.

But as the Copilot+ PC world starts to get a little more confusing, this can all be boiled down to one question you need to ask yourself: do you want compatibility comfort at the expense of worry-free battery life, or sit through the transition of several Windows apps to Arm and embrace a more perfect balance between power and stamina with a Snapdragon system?

And the more I think about it, the more Im realizing that there may only be one correct answer here

This is our first step into AMDs Strix Point, so lets get to the (ahem) point. Yes, AMD Ryzen AI 9 HX 370 is a powerhouse in plenty of areas that puts the M3 MacBook Air on notice. Not only that, but Asus attention to the aesthetics and ergonomics makes this rather nice to use.

Asus struck gold with its design refresh involving these clean lines and utilitarian branding, and the Zenbook S 16 continues this trend with a gorgeously sophisticated notebook. It all starts with that ceraluminum finish (PR-speak for a ceramic/aluminum composite), which gives the lid an incredible textured finish that feels great to the touch and eliminates any fingerprints.

Its also impressively thin and light for a 16-inch laptop, though the MacBook Air does pip it in thinness and the Surface Laptop 7 edges below in weight.

Then you open it up, and youre greeted by a mouthwatering OLED screen (more on that later) alongside a fantastic keyboard/touchpad combination. In some ways, the touchpad reminds me of the Huawei MateBook X Pro giving you controls over the brightness, volume and video scrubbing along the edges of it along with a smooth multi-touch surface.

Meanwhile, the keyboard is nicely spaced out with plenty of comfortable travel on each individual key. Put simply, youll really enjoy getting stuff done on here.

I was recently an OLED convert, and the Zenbook S 16 continues my love affair with the panel technology a flash flood of accurate color for all your productivity and entertainment purposes. While LCD continues to pip it in terms of brightness, Id happily give up a little bit of that in favor of this vividity.

In terms of that accuracy, the MacBook Airs Liquid Retina panel does come close enough that you wont really tell the difference in the sRGB gamut. But its in things like watching super colorful shows or making the most of that deep contrast ratio where the S 16 really comes into its own.

So lets dabble with the AMD Ryzen AI 9 HX 370 in here. With 12 cores and 24 threads clocked at up to 5.1 GHz, it is capable of handily defeating the M3 MacBook Air and even comes close to taking on M3 Pro. When compared to Snapdragon X Elite, the picture is a little bit mixed, but were entering an era where Windows laptops are becoming more capable of taking on Apple silicon.

Where it does beat Snapdragon, however, is in three key areas:

Of course, these 3DMark results may be skewed slightly by the fact that they rely on x86 architecture, which means the Surface Laptop 7s Snapdragon X Elite will have to run it through Prism translation. However, this is a realistic representation given the job of rebuilding a lot of apps to make the most of Arm is well underway and will take a while to finish. So a compromise in performance is expected.

A complicated picture is being painted by the new AMD chips here. On one hand, they are indeed more powerful in certain areas. But its almost as if the Zenbook S 16 is being slightly hamstrung from really competing with the Arm likes of Snapdragon X Elite and Apple silicon.

If we were in a world where all Windows laptops were x86 and Microsofts Arm efforts were still a bit of a joke, I would be here telling you how the battery life has improved over the likes of Intel Core Ultra (which it has).

But were not. Were in a new era where Windows 11 systems are capable of outlasting MacBooks, so its time to alter expectations:

As you can see, the not-so-power-efficient nature of x86 means it falls behind its competition by at least a couple of hours.

Plus, in what seems to be a symptom of most Copilot+ PCs weve tested, while thermal management has improved over last generation chips, they do still get noticeably hotter than Apples notebooks.

As you saw from the performance charts up above, the margins between AMD and Qualcomm are fine, but there is a difference here in Geekbench scores, SSD transfer speeds and the way it handles transcoding video.

In practice, these wont be the biggest dips in performance in real world use. I experienced extremely little slowdown under intense multitasking pressure. But the numbers dont lie, and with both the Arm Surface Laptop 7 and x86 Zenbook S 16 coming in at near-identical prices, you are getting a slightly better price-to-performance ratio when it comes to tackling multi-core tasks and loading up big files.

Copilot+ PCs are entering their confusing era, as not every chip will give you the same experience youd expect from reading our current crop of reviews.

The Asus Zenbook S 16 is, in many ways, a good laptop. The OLED display is a spectacle encased in that beautiful ceraluminum shell with a top notch keyboard and touchpad. Not only that, but sticking to x86 gives you no issues with app compatibility while Windows developers scramble to create Arm versions.

However, you cant stop the feeling that maybe, just maybe, Strix Point plays second fiddle to Snapdragon X Elite. I mean there are some areas where it reigns supreme, such as integrated graphics and AI processing with that larger NPU.

But the Arm variant of Copilot+ PCs strikes a better balance between performance and power efficiency something that I would pick even though running into some apps that just dont work yet can be frustrating.

Original post:

I just tested Asus first AMD Ryzen AI Copilot+ PC can it beat Snapdragon X Elite? Sort of - Tom's Guide

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The U.S. Department of Commerce announced today, on the 270-day mark since President Bidens Executive Order (EO) on the Safe, Secure and Trustworthy Development of AI, the release of new guidance and software to help improve the safety, security and trustworthiness of artificial intelligence (AI) systems.

The departments National Institute of Standards and Technology (NIST) released three final guidance documents that were first released in April for public comment, as well as a draft guidance document from the U.S. AI Safety Institute that is intended to help mitigate risks. NIST is also releasing a software package designed to measure how adversarial attacks can degrade the performance of an AI system. In addition, Commerces U.S. Patent and Trademark Office (USPTO) issued a guidance update on patent subject matter eligibility to address innovation in critical and emerging technologies, including AI.

For all its potentially transformational benefits, generative AI also brings risks that are significantly different from those we see with traditional software. These guidance documents and testing platform will inform software creators about these unique risks and help them develop ways to mitigate those risks while supporting innovation. Laurie E. Locascio, Under Secretary of Commerce for Standards and Technology and NIST Director

Read the full Department of Commerce news release.

Read the White House fact sheet on administration-wide actions on AI.

The NIST releases cover varied aspects of AI technology. Two of them appear today for the first time: One is the initial public draft of a guidance document from the U.S. AI Safety Institute, and is intended to help software developers mitigate the risks stemming from generative AI and dual-use foundation models AI systems that can be used for either beneficial or harmful purposes. The other is a testing platform designed to help AI system users and developers measure how certain types of attacks can degrade the performance of an AI system.

Of the remaining three releases, two are guidance documents designed to help manage the risks of generative AI the technology that enables many chatbots as well as text-based image and video creation tools and serve as companion resources to NISTs AI Risk Management Framework (AI RMF) and Secure Software Development Framework (SSDF). The third proposes a plan for U.S. stakeholders to work with others around the globe on AI standards. These three publications previously appeared April 29 in draft form for public comment, and NIST is now releasing their final versions.

The two releases NIST is announcing today for the first time are:

AI foundation models are powerful tools that are useful across a broad range of tasks and are sometimes called dual-use because of their potential for both benefit and harm. NISTs AI Safety Institute has released the initial public draft of its guidelines on Managing Misuse Risk for Dual-Use Foundation Models (NIST AI 800-1), which outlines voluntary best practices for how foundation model developers can protect their systems from being misused to cause deliberate harm to individuals, public safety and national security.

The draft guidance offers seven key approaches for mitigating the risks that models will be misused, along with recommendations for how to implement them and how to be transparent about their implementation. Together, these practices can help prevent models from enabling harm through activities like developing biological weapons, carrying out offensive cyber operations, and generating child sexual abuse material and nonconsensual intimate imagery.

NIST is accepting comments from the public on the draft Managing the Risk of Misuse for Dual-Use Foundation Models until Sept. 9, 2024, at 11:59 p.m. Eastern Time. Comments can be submitted to NISTAI800-1 [at] nist.gov (NISTAI800-1[at]nist[dot]gov).

One of the vulnerabilities of an AI system is the model at its core. By exposing a model to large amounts of training data, it learns to make decisions. Butif adversaries poison the training data with inaccuracies for example, by introducing data that can cause the model to misidentify stop signs as speed limit signs the model can make incorrect, potentially disastrous decisions. Testing the effects of adversarial attacks on machine learning models is one of the goals of Dioptra, a new software package aimed at helping AI developers and customers determine how well their AI software stands up to a variety of adversarial attacks.

The open-source software, available for freedownload, could help the community, including government agencies and small to medium-sized businesses, conduct evaluations to assess AI developers claims about their systems performance. This software responds to Executive Order section 4.1 (ii) (B), which requires NIST to help with model testing. Dioptra does this by allowing a user to determine what sorts of attacks would make the model perform less effectively and quantifying the performance reduction so that the user can learn how often and under what circumstances the system would fail.

Augmenting todays two initial releases are three finalized documents:

The AI RMF Generative AI Profile (NIST AI 600-1) can help organizations identify unique risks posed by generative AI and proposes actions for generative AI risk management that best aligns with their goals and priorities. The guidance is intended to be a companion resource for users of NISTsAI RMF. It centers on a list of 12 risks and just over 200 actions that developers can take to manage them.

The 12 risks include a lowered barrier to entry for cybersecurity attacks, the production of mis- and disinformation or hate speech and other harmful content, and generative AI systems confabulating or hallucinating output. After describing each risk, the document presents a matrix of actions that developers can take to mitigate it, mapped to the AI RMF.

The second finalized publication, Secure Software Development Practices for Generative AI and Dual-Use Foundation Models (NISTSpecial Publication (SP) 800-218A), is designed to be used alongside the Secure Software Development Framework (SP 800-218). While the SSDF is broadly concerned with software coding practices, the companion resource expands the SSDF in part to address a major concern with generative AI systems: They can becompromised with malicious training data that adversely affect the AI systems performance.

In addition to covering aspects of the training and use of AI systems, this guidance document identifies potential risk factors and strategies to address them. Among other recommendations, it suggests analyzing training data for signs of poisoning, bias, homogeneity and tampering.

AI systems are transforming society not only within the U.S., but around the world. A Plan for Global Engagement on AI Standards (NIST AI 100-5), todays third finalized publication, is designed to drive the worldwide development and implementation of AI-related consensus standards, cooperation and coordination, and information sharing.

The guidance is informed by priorities outlined in the NIST-developed Plan for Federal Engagement in AI Standards and Related Tools and is tied to the National Standards Strategy for Critical and Emerging Technology. This publication suggests that a broader range of multidisciplinary stakeholders from many countries participate in the standards development process.

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Department of Commerce Announces New Guidance, Tools 270 Days Following President Bidens Executive Order on AI - NIST