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CRNs inaugural AI 100 list breaks down the 100 companies you need to know about in the AI market in the five major categories: cloud, security, data and analytics, data center and edge, and software.

Artificial intelligence is revolutionizing the IT industry with the most powerful tech companies on the planet, along with some special unicorn startups, investing significantly in AI and generative AI.

There are 100 companies who are standing out from the crowd by leading the AI revolution in 2024 by providing AI solutions for cloud computing, cybersecurity, data and analytics, edge computing, data centers, PCs and software.

From Nvidia and Microsoft to Dell Technologies and Wiz, these 100 artificial intelligence market leaders are the ones making waves in the boiling-hot AI industry with billions expected to be poured into new AI technology this year.

CRNs inaugural AI 100 list breaks down the 100 companies you need to know about in the AI market in the five major categories: cloud, security, data and analytics, data center and edge, and software. Before jumping into the list of 100 AI companies, heres what to know about each category.

AI For Cloud

Cloud computing is one of the most popular delivery options for AI and generative AI applications at scale, with companies like AWS, IBM, Google, Microsoft and Oracle making the list as well as several cloud startups.

The rapid advancement in large language models (LLMs) and foundation models (FMs) are driving AI and GenAI capabilities and business use cases with many cloud companies leading the way.

Because of its scale and shared services model, cloud technology is best-suited for the delivery of GenAI-enabled applications at scale and the development of general-purpose foundation models, said Gartner Analyst Sig Nag in a recent report.

AI For Cybersecurity

Both AI and machine learning technologies are under the hood of just about every major cybersecurity capability out therefrom threat detection, to user authentication and access management, to analysis of network traffic and many other key security functions.

GenAI has gone viral in the cybersecurity industry, with countless vendors introducing new capabilities powered by LLMs in the wake of OpenAIs debut of ChatGPT in late 2022. In the security landscape including email, collaboration, code security, protecting the use of GenAI itselfproducts are being enhanced with the help of AI.

Cybersecurity firms securing AI on various fronts range from CrowdStrike and Palo Alto Networks to Wiz and Vectra AI.

AI For Data Center, PC And Edge

AI is powered by processors, servers and data storage devices, while PCs and laptops have become one of the hottest AI topics in 2024.

From data centers to edge computing and end-user PCs, AI is infiltrating across the IT landscape in the shape on new CPUs, AI-powered data management as well as AI copilots initiatives to boost AIOps.

In this category, AI superstars like Nvidia, Cisco, Intel, HPE and Supermicro made the list alongside AI startups such as Prosimo and Run:ai.

AI For Data And Analytics

Data is the fuel that AI needs to work. IT companies in the data management spaceincluding suppliers of tools for collecting, managing and preparing huge volumes of dataplay a huge role in the AI wave sweeping the industry.

Also participating in the AI tsunami are many data analytics companies, both established vendors and startups, that are incorporating AI and generative AI into their software to go beyond traditional business intelligence and reporting to provide sophisticated, AI-powered search and analytics tools and natural language querying capabilities.

Data and analytics all-stars who made CRNs list include Alteryx, Couchbase, Databricks, Dataloop and Informatica, to name a few.

AI For Software

From AI virtual assistants to the automation of administrative IT tasks, AI software companies are critical to the industry.

Early AI software use cases focus on improving operations for businesses, creating new revenue streams and boosting productivity such as AI for generative code and content.

Research firm IDC predicts enterprises will spend over $38 billion worldwide on generative AI software and related infrastructure hardware and services, with the number reaching $151 billion by 2027

Companies driving AI for software include the likes of Connectwise, CrushBank, Dataiku, SAP and ServiceNow.

For CRNs inaugural AI 100 list, we highlight the work 100 top-notch AI providers are doing that is powering the AI and GenAI world of the future.

The 25 Hottest AI Companies For Data Center And Edge: The 2024 CRN AI 100 Here are the 25 companies leading the AI revolution and innovation race in servers, chips, networking, storage, microprocessors, laptops and PCs in 2024.

The 20 Hottest AI Cloud Companies: The 2024 CRN AI 100 From AWS and Microsoft Azure to Altair, MongoDB and Dynatrace, here are the 20 cloud AI companies you need to know about in 2024.

The 20 Hottest AI Cybersecurity Companies: The 2024 CRN AI 100 The coolest AI cybersecurity companies in 2024 include Crowdstrike, Fortinet, Netskope and Trend Micro.

The 20 Hottest AI Software Companies: The 2024 CRN AI 100 The coolest AI software companies in 2024 include CrushBank, LogicMonitor, MSPbots, Rewst and more.

The 15 Hottest AI Data And Analytics Companies: The 2024 CRN AI 100 Here are 15 data management and data analytics companies, part of the inaugural CRN AI 100, that are playing an outsized role in AI today.

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AI Companies With A Winning Hand: The 2024 CRN AI 100 - CRN

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Students sitting for their STAAR exams this week will be part of a new method of evaluating Texas schools: Their written answers on the states standardized tests will be graded automatically by computers.

The Texas Education Agency is rolling out an automated scoring engine for open-ended questions on the State of Texas Assessment of Academic Readiness for reading, writing, science and social studies. The technology, which uses natural language processing, a building block of artificial intelligence chatbots such as GPT-4, will save the state agency about $15-20 million per year that it would otherwise have spent on hiring human scorers through a third-party contractor.

The change comes after the STAAR test, which measures students understanding of state-mandated core curriculum, was redesigned in 2023. The test now includes fewer multiple choice questions and more open-ended questions known as constructed response items. After the redesign, there are six to seven times more constructed response items.

We wanted to keep as many constructed open ended responses as we can, but they take an incredible amount of time to score, said Jose Rios, director of student assessment at the Texas Education Agency.

In 2023, Rios said TEA hired about 6,000 temporary scorers, but this year, it will need under 2,000.

To develop the scoring system, the TEA gathered 3,000 responses that went through two rounds of human scoring. From this field sample, the automated scoring engine learns the characteristics of responses, and it is programmed to assign the same scores a human would have given.

This spring, as students complete their tests, the computer will first grade all the constructed responses. Then, a quarter of the responses will be rescored by humans.

When the computer has low confidence in the score it assigned, those responses will be automatically reassigned to a human. The same thing will happen when the computer encounters a type of response that its programming does not recognize, such as one using lots of slang or words in a language other than English.

We have always had very robust quality control processes with humans, said Chris Rozunick, division director for assessment development at the Texas Education Agency. With a computer system, the quality control looks similar.

Every day, Rozunick and other testing administrators will review a summary of results to check that they match what is expected. In addition to low confidence scores and responses that do not fit in the computers programming, a random sample of responses will also be automatically handed off to humans to check the computers work.

TEA officials have been resistant to the suggestion that the scoring engine is artificial intelligence. It may use similar technology to chatbots such as GPT-4 or Googles Gemini, but the agency has stressed that the process will have systematic oversight from humans. It wont learn from one response to the next, but always defer to its original programming set up by the state.

We are way far away from anything thats autonomous or can think on its own, Rozunick said.

But the plan has still generated worry among educators and parents in a world still weary of the influence of machine learning, automation and AI.

Some educators across the state said they were caught by surprise at TEAs decision to use automated technology also known as hybrid scoring to score responses.

There ought to be some consensus about, hey, this is a good thing, or not a good thing, a fair thing or not a fair thing, said Kevin Brown, the executive director for the Texas Association of School Administrators and a former superintendent at Alamo Heights ISD.

Representatives from TEA first mentioned interest in automated scoring in testimony to the Texas House Public Education Committee in August 2022. In the fall of 2023, the agency announced the move to hybrid scoring at a conference and during test coordinator training before releasing details of the process in December.

The STAAR test results are a key part of the accountability system TEA uses to grade school districts and individual campuses on an A-F scale. Students take the test every year from third grade through high school. When campuses within a district are underperforming on the test, state law allows the Texas education commissioner to intervene.

The commissioner can appoint a conservator to oversee campuses and school districts. State law also allows the commissioner to suspend and replace elected school boards with an appointed board of managers. If a campus receives failing grades for five years in a row, the commissioner is required to appoint a board of managers or close that school.

With the stakes so high for campuses and districts, there is a sense of uneasiness about a computers ability to score responses as well as a human can.

There's always this sort of feeling that everything happens to students and to schools and to teachers and not for them or with them, said Carrie Griffith, policy specialist for the Texas State Teachers Association.

A former teacher in the Austin Independent School District, Griffith added that even if the automated scoring engine works as intended, it's not something parents or teachers are going to trust.

Superintendents are also uncertain.

The automation is only as good as what is programmed, said Lori Rapp, superintendent at Lewisville ISD. School districts have not been given a detailed enough look at how the programming works, Rapp said.

The hybrid scoring system was already used on a limited basis in December 2023. Most students who take the STAAR test in December are retaking it after a low score. Thats not the case for Lewisville ISD, where high school students on an altered schedule test for the first time in December, and Rapp said her district saw a drastic increase in zeroes on constructed responses.

At this time, we are unable to determine if there is something wrong with the test question or if it is the new automated scoring system, Rapp said.

The state overall saw an increase in zeroes on constructed responses in December 2023, but the TEA said there are other factors at play. In December 2022, the only way to score a zero was by not providing an answer at all. With the STAAR redesign in 2023, students can receive a zero for responses that may answer the question but lack any coherent structure or evidence.

The TEA also said that students who are retesting will perform at a different level than students taking the test for the first time. Population difference is driving the difference in scores rather than the introduction of hybrid scoring, a TEA spokesperson said in an email.

For $50, students and their parents can request a rescore if they think the computer or the human got it wrong. The fee is waived if the new score is higher than the initial score. For grades 3-8, there are no consequences on a students grades or academic progress if they receive a low score. For high school students, receiving a minimum STAAR test score is a common way to fulfill one of the state graduation requirements, but it is not the only way.

Even with layers of quality control, Round Rock ISD Superintendent Hafedh Azaiez said he worries a computer could miss certain things that a human being may not be able to miss, and that room for error will impact students who Azaiez said are trying to do his or her best.

Test results will impact how they see themselves as a student, Brown said, and it can be humiliating for students who receive low scores. With human graders, Brown said, students were rewarded for having their own voice and originality in their writing, and he is concerned that computers may not be as good at rewarding originality.

Julie Salinas, director of assessment, research and evaluation at Brownsville ISD said she has concerns about whether hybrid scoring is allowing the students the flexibility to respond in a way that they can demonstrate their full capability and thought process through expressive writing.

Brownsville ISD is overwhelmingly Hispanic. Students taking an assessment entirely in Spanish will have their tests graded by a human. If the automated scoring engine works as intended, responses that include some Spanish words or colloquial, informal terms will be flagged by the computer and assigned to a human so that more creative writing can be assessed fairly.

The system is designed so that it does not penalize students who answer differently, who are really giving unique answers, Rozuick said.

With the computer scoring now a part of STAAR, Salinas is focused on adapting. The district is incorporating tools with automated scoring into how teachers prepare students for the STAAR test to make sure they are comfortable.

Our district is on board and on top of the things that we need to do to ensure that our students are successful, she said.

Disclosure: Google, the Texas Association of School Administrators and Texas State Teachers Association have been financial supporters of The Texas Tribune, a nonprofit, nonpartisan news organization that is funded in part by donations from members, foundations and corporate sponsors. Financial supporters play no role in the Tribune's journalism. Find a complete list of them here.

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Texas will use computers to grade written answers on this year's STAAR tests - The Texas Tribune

For beauty shoppers, it was already hard enough to trust reviews online.

Brands such as Sunday Riley and Kylie Skin are among those to have been caught up in scandals over fake reviews, with Sunday Riley admitting in a 2018 incident that it had tasked employees with writing five-star reviews of its products on Sephora. It downplayed the misstep at the time, arguing it would have been impossible to post even a fraction of the hundreds of thousands of Sunday Riley reviews on platforms around the globe.

Today, however, thats increasingly plausible with generative artificial intelligence.

Text-generating tools like ChatGPT, which hit the mainstream just over a year ago, make it easier to mimic real reviews faster, better and at greater scale than ever before, creating more risk of shoppers being taken in by bogus testimonials. Sometimes there are dead giveaways. As an AI language model, I dont have a body, but I understand the importance of comfortable clothing during pregnancy, began one Amazon review of maternity shorts spotted by CNBC. But often theres no way to know.

Back in the day, you would see broken grammar and youd think, That doesnt look right. That doesnt sound human, said Saoud Khalifah, a former hacker and founder of Fakespot, an AI-powered tool to identify fake reviews. But over the years weve seen that drop off. These fake reviews are getting much, much better.

Fake reviews have become an industry in themselves, driven by fraud farms that act as syndicates, according to Khalifah. A 2021 report by Fakespot found roughly 31 percent of reviews across Amazon, Sephora, Walmart, eBay, Best Buy and sites powered by Shopify which altogether accounted for more than half of US online retail sales that year to be unreliable.

It isnt just bots that are compromising trust in beauty reviews. The beauty industry already relies heavily on incentivised human reviewers, who receive a free product or discount in exchange for posting their opinion. It can be a valuable way for brands to get new products into the hands of their target audience and boost their volume of reviews, but consumers are increasingly suspicious of incentivised reviews, so brands should use them strategically, and should always explicitly declare them.

Sampling and review syndicators such as Influenster are keen to point out that receiving a free product does not oblige the reviewer to give positive feedback, but its clear from the exchanges in online communities that many users of these programmes believe they will receive more freebies if they write good reviews. As one commenter wrote in a post in Sephoras online Beauty Insider community, People dont want to stop getting free stuff if they say honest or negative things about the products they receive for free.

That practice alone can skew the customer rating of a product. On Sephora, for example, the new Ouai Hair Gloss In-Shower Shine Treatment has 1,182 reviews and a star rating of 4.3. But when filtering out incentivised reviews, just 89 remain. Sephora also doesnt recalculate the star rating after removing those reviews. Among just the non-incentivised reviews, the products rating is 2.6 stars. The issue has sparked some frustration among members of its online community. Sephora declined to comment.

But the situation gets even murkier when factoring in the rise in reviews partially created by a human and partially by AI. Khalifah describes these kinds of reviews as a hybrid monstrosity, where its half legit and half not, because AI is being used to fill the gaps within the review and make it look better.

The line between authentic reviews and AI-generated content is itself beginning to blur as review platforms roll out new AI-powered tools to assist their communities in writing reviews. Bazaarvoice, a platform for user-generated content which owns Influenster and works with beauty brands including LOral, Pacifica, Clarins and Sephora, has recently launched three new AI-powered features, including a tool called Content Coach. The company developed the tool based on research showing that 68 percent of its community had trouble getting started when writing a review, according to Marissa Jones, Bazaarvoice senior vice president of product.

Content Coach gives users prompts of key topics to include in their review, based on common themes in other reviews. The prompts for a review of a Chanel eyeliner might include pigmentation, precision and ease of removal, for instance. As users type their review, the topic prompts light up as they are addressed, gamifying the process.

Jones stressed that the prompts are meant to be neutral. We wanted to provide an unbiased way to give [users] some ideas, she said. We dont want to influence their opinion or do anything that pushes them one direction or the other.

But even such seemingly innocuous AI nudges as those created by Content Coach can still influence what a consumer writes in a product review, shifting it from a spontaneous response based on considered appraisal of a product to something more programmed that requires less thought.

Fakespots Khalifah points out that governments and regulators around the globe have been slow to act, given the speed at which the problem of fake reviews is evolving with the advancement of generative AI.

But change is finally on the horizon. In July 2023, the US Federal Trade Commission introduced the Trade Regulation Rule on the Use of Consumer Reviews and Testimonials, a new piece of regulation to punish marketers who feature fake reviews, suppress negative reviews or offer incentives for positive ones.

Our proposed rule on fake reviews shows that were using all available means to attack deceptive advertising in the digital age, Samuel Levine, director of the FTCs Bureau of Consumer Protection, said in a release at the time. The rule would trigger civil penalties for violators and should help level the playing field for honest companies.

In its notice of proposed rule-making, the FTC shared comments from industry players and public interest groups on the damage to consumers caused by fake reviews. Amongst these, the National Consumers League cited an estimate that, in 2021, fraudulent reviews cost US consumers $28 billion. The text also noted that the widespread emergence of AI chatbots is likely to make it easier for bad actors to write fake reviews.

In beauty, of course, the stakes are potentially higher, as fake reviews can also mislead consumers into buying counterfeit products, which represent a risk to a shoppers health and wellbeing as well as their wallet.

If the FTCs proposed rule gets the green light, as expected, it will impose civil penalties of up to $51,744 per violation. The FTC could take the position that each individual fake review constitutes a separate violation every time it is viewed by a consumer, establishing a considerable financial deterrent to brands and retailers alike.

With this tougher regulatory stance approaching, beauty brands should get their houses in order now, and see it as an opportunity rather than an imposition. There is huge potential for brands and retailers to take the lead on transparency and build an online shopping experience consumers can believe in.

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Beauty Reviews Were Already Suspect. Then Came Generative AI. - The Business of Fashion

IN THE run-up to Ukraines rocket attacks on the Antonovsky Bridge, a vital road crossing from the occupied city of Kherson to the eastern bank of the Dnipro River, security officials carefully studied a series of special reports. It was the summer of 2022 and Russia was relying heavily on the bridge to resupply its troops west of the Dnipro. The reports contained research into two things: would destroying the bridge lead the Russian soldiers, or their families back home, to panic? And, more importantly, how could Ukraines government maximise the blow to morale by creating a particular information environment?

This is how Sviatoslav Hnizdovsky, the founder of the Open Minds Institute (OMI) in Kyiv, describes the work his research outfit did by generating these assessments with artificial intelligence (AI). Algorithms sifted through oceans of Russian social-media content and socioeconomic data on things ranging from alcohol consumption and population movements to online searches and consumer behaviour. The AI correlated any changes with the evolving sentiments of Russian loyalists and liberals over the potential plight of their countrys soldiers.

This highly sensitive work continues to shape important Ukrainian decisions about the course of the war, says Mr Hnizdovsky. This includes potential future strikes on Russias Kerch Bridge, which is the only direct land link between Russia and Crimea.

Ukraine, outgunned by Russia, is increasingly seeking an edge with AI by employing the technology in diverse ways. A Ukrainian colonel involved in arms development says drone designers commonly query ChatGPT as a start point for engineering ideas, like novel techniques for reducing vulnerability to Russian jamming. Another military use for AI, says the colonel, who requested anonymity, is to identify targets.

As soldiers and military bloggers have wisely become more careful with their posts, simple searches for any clues about the location of forces have become less fruitful. By ingesting reams of images and text, however, AI models can find potential clues, stitch them together and then surmise the likely location of a weapons system or a troop formation. Using this puzzle-pieces approach with AI allows Molfar, an intelligence firm with offices in Dnipro and Kyiv, to typically find two to five valuable targets every day, says Maksym Zrazhevsky, an analyst with the firm. Once discovered, this intelligence is quickly passed along to Ukraines army, resulting in some of the targets being destroyed.

Targeting is being assisted by AI in other ways. SemanticForce, a firm with offices in Kyiv and Ternopil, a city in the west of Ukraine, develops models that in response to text prompts scrutinises online or uploaded text and images. Many of SemanticForces clients use the system commercially to monitor public sentiments about their brands. Molfar, however, uses the model to map areas where Russian forces are likely to be low on morale and supplies, which could make them a softer target. The AI finds clues in pictures, including those from drone footage, and from soldiers bellyaching in social media.

It also cobbles together clues about Russian military weaknesses using a sneaky proxy. For this, Molfar employs SemanticForces AI to generate reports on the activities of Russian volunteer groups that fundraise and prepare care packages for the sections of the front most in need. The algorithms, Molfar says, do a good job of discarding potentially misleading bot posts. (Accounts with jarring political flip-flops are one tipoff.) The firms analysts sometimes augment this intelligence by using software that disguises the origin of a phone call, so that Russian volunteer groups can be rung by staff pretending to be a Russian eager to contribute. Ten of the companys 45-odd analysts work on targeting, and do so free of charge for Ukrainian forces.

Then there is counter-intelligence. The use of AI helps Ukraines spycatchers identify people who Oleksiy Danilov, until recently secretary of the National Security and Defence Council (NSDC), describes as prone to betrayal. Offers to earn money by taking geolocated pictures of infrastructure and military assets are often sent to Ukrainian phones, says Dmytro Zolotukhin, a former Ukrainian deputy minister for information policy. He recently received one such text himself. People who give this market for intelligence services a shot, he adds, are regularly nabbed by Ukraines SBU intelligence agency.

Using AI from Palantir, an American firm, Ukrainian counter-intelligence fishes for illuminating linkages in disparate pools of data. Imagine, for instance, an indebted divorcee at risk of losing his flat and custody of his children who opens a foreign bank account and has been detected with his phone near a site that was later struck by missiles. In addition to such dot-connecting, the AI performs social-network analysis. If, say, the hypothetical divorcee has strong personal ties to Russia and has begun to take calls from someone whose phone use suggests a higher social status, then AI may increase his risk score.

The result of AI assessments of interactions among a networks nodes have been impressive for more than a decade. Kristian Gustafson, a former British intelligence officer who advised Afghanistans interior ministry in 2013, recounts the capture of a courier transporting wads of cash for Taliban bigwigs. Their ensuing phone calls, he says, lit up the whole diagram. Since then, algorithmic advances for calculating things like betweenness centrality, a measure of influence, make those days look, as another former intelligence officer puts it, pretty primitive.

In addition, network analysis helps Ukrainian investigators identify violators of sanctions on Russia. By connecting data in ship registries with financial records held elsewhere, the software can pierce the corporate veil, a source says. Mr Zolotukhin says hackers are providing absolutely enormous caches of stolen business data to Ukrainian agencies. This is a boon for combating sanctions-busting.

The use of AI has been developing for some time. Volodymyr Zelensky, Ukraines president, called for the development of a massive boost in the use of the technology for national security in November 2019. The result is a strategically minded model built and run by the NSDC that ingests text, statistics, photos and video. Called the Centre of Operations for Threats Assessment (COTA), it is fed a wide range of information, some obtained by hackers, says Andriy Ziuz, NSDCs head of staff. The model tracks prices, phone usage, migration, trade, energy, politics, diplomacy and military developments down to the weapons in repair shops.

Operators at COTA call this model a constructor. This is because it also ingests output from smaller models such as Palantirs software and Delta, which is battlefield software that supports the Ukrainian armys manoeuvre decisions. COTAs bigger picture output provides senior officials with guidance on sensitive matters, including mobilisation policy, says Mykola Dobysh, NSDCs chief technologist. Mr Danilov notes that Mr Zelensky has been briefed on COTAs assessments on more than 130 occasions, once at 10am on the day of Russias full invasion. Access to portions (or circuits) of COTA is provided to some other groups, including insurers, foreign ministries and Americas Department of Energy.

Ukraines AI effort benefits from its societys broad willingness to contribute data for the war effort. Citizens upload geotagged photos potentially relevant for the countrys defence into a government app called Diia (Ukrainian for action). Many businesses supply Mantis Analytics, a firm in Lviv, with operations data on things that range from late deliveries to call-centre activity and the setting off of burglar alarms. Recipients of the platforms assessments of societal functioning include the defence ministry and companies that seek to deploy their own security resources in better ways.

How much difference all this will ultimately make is still unclear. Evan Platt of Zero Line, an NGO in Kyiv that provides kit to troops and who spends time at the front studying fighting effectiveness, describes Ukraines use of AI as a bright spot. But there are concerns. One is that enthusiasm for certain AI security applications may divert resources that would provide more bang for the buck elsewhere. Excessive faith in AI is another risk, and some models on the market are certainly overhyped. More dramatically, might AI prove to be a net negative for Ukraines battlefield performance?

A few think so. One is John Arquilla, a professor emeritus at the Naval Postgraduate School in California who has written influential books on warfare and advised Pentagon leaders. Ukraines biggest successes came early in the war when decentralised networks of small units were encouraged to improvise. Today, Ukraines AI constructor process, he argues, is centralising decision-making, snuffing out creative sparks at the edges. His assessment is open to debate. But at a minimum, it underscores the importance of human judgment in how any technology is used.

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How Ukraine is using AI to fight Russia - The Economist