Knowledge graphs reduce hallucinations, he says, but they also help solve the explainability challenge. Knowledge graphs sit on top of traditional databases, providing a layer of connection and deeper understanding, says Anant Adya, EVP at Infosys. “You can do better contextual search,” he says. “And it helps you drive better insights.” Infosys is now running proof of concepts to use knowledge graphs to combine the knowledge the company has gathered over many years with gen AI tools. “We’re identifying those use cases where they can make a bigger impact,” he says. They include automated knowledge extraction, budgeting, procurement, and enterprise planning. “But it’s very early,” he adds. “It’s still not in production.” One company that’s deployed a knowledge graph to improve gen AI performance, and wrote about, is LinkedIn. In a paper published in April, LinkedIn reports that combining RAG with a knowledge graph helped it improve the accuracy of a customer service gen AI application by 78%. And, over the preceding six months, the combo was used by LinkedIn’s customer service team, reducing the median per-issue resolution time by 29%. source

One of the oldest and largest oil and gas companies in the world, bp is in the midst of a major transition as it pivots toward becoming an integrated energy company. As part of this transition, the company is aiming for a net-zero carbon footprint by 2050. The scope of its efforts so far is demonstrated by its shift into lower-carbon businesses, power trading, and convenience stores, which represented just 3% of its investment in 2019 but 23% in 2023. This change in business focus is accompanied by an ongoing digital transformation. A key part of this is to use AI to help improve its operations. From COBOL to EVs: The scope of bp’s digital initiatives As the energy industry goes through multiple mergers and acquisitions over the years, it deals with a wide spectrum of digital apps, legacy systems, and business processes across its functions. While some of these are modern, cutting-edge elements of the tech stack, they also carry the load of outdated technologies that require transformation. In bp’s case, the multiple generations of IT hardware and software have been made even more complex by the scope and variety of the company’s operations, from oil exploration to electric vehicle (EV) charging machines to the ordinary office activities of a corporation. Historically, AI use has been focused on machine learning in operations such as exploration and drilling in the initial phases of energy production. Now, however, generative AI (genAI) and other forms of digital innovation are helping drive efficiencies closer to the end customer. The reality for a company of bp’s size and history is that there are different levels of technology maturity in different areas of the business. This reflects both the diversity in the technical infrastructure as well as the readiness to experiment of different operating units. Focusing on business value before AI Mariza Fotiou, VP for digital product management at bp, emphasizes the importance of focusing on the business value that is being sought, whether AI is involved or not. “The first thing to look at,” she says, “is the problem I’m trying to solve, and the solution I can create to generate value with the least complexity.” In that context, she adds: “We’re not looking at AI use cases [per se]. We are looking at bp problems or customer problems that we need to solve that AI can accelerate.” Fotiou draws on her background in product development and digital transformation—first in the finance sector and then in bp’s upstream operations—to help solve downstream challenges in the B2B space, especially in mobility and fleet operations. With such a wide variety of products in development across the company, finding the best ones for AI requires prioritization. Fotiou and her colleagues particularly look to “parts of the business that have an intense amount of data that we’re trying to manage, because that’s where AI can drive the most value.” Engagement with leadership and upskilling for personnel help “develop the conditions for AI innovation and experimentation to take place,” she says. Along the way, the company decides whether to build or buy a solution for each use case. To guide that decision, bp applies consistent design governance principles to find the solutions—always grounded in safety—that are most competitive, optimal in terms of cost, and likeliest to provide the company with a differentiating advantage. “If it makes sense for us to create and maintain the IP,” Fotiou explains, “then we will go off and build something. If we are lagging and just playing catch-up, we might as well buy it. But cost is always a big part of the equation that we need to consider.” Fotiou has found that in some cases open-source tools can help especially with cost considerations. For example, her team is leveraging open-source AI product management tools to help define thousands of product requirements as they replace their fleet management system. “We’re leveraging AI as much as we can around product development,” she says, “and it seems to be quite helpful.” How bp Is putting AI to work “We have found ways to use AI across all these value streams,” Fotiou says, “from helping produce energy to trading the energy, all the way to supplying and distributing the energy.” AI has helped bp identify the best locations for placing EV charging stations for customers to use them, and it has enhanced the company’s award-winning safe2go fuel data platform, which uses computer vision to ensure that aircraft receive the correct fuel. Like many companies, bp is also using genAI to extract information from documents, summarize meetings, and so on, freeing up office workers’ time for more strategic activities. And it uses AI to automate code testing and other aspects of the digital development lifecycle. In 2023, Infosys became bp’s main partner for end-to-end application services, helping to transform bp’s digital application landscape. Infosys, among other partners, is helping bp implement a variety of AI projects across different areas of its operations, and is working with Fotiou and her team on several legacy applications in bp’s fleet management suite. These apps have millions of lines of code; Infosys is using genAI to help analyze this code and deliver precise recommendations for refactoring, code conversion, documentation, automated test cases, and test scripts. The work accelerates feature development on the codebase and creates a robust knowledge management repository to reduce artifact creation efforts by more than 70%. This refactoring helps to set the base for digital transformation. Solving problems for many kinds of customers It is evident from these examples that bp is leveraging AI across many facets of its business. Whether in production management, application modernization, fleet fueling, or retail store operations, AI can help improve efficiency in delivery and help support the business transformation that drives value creation. “This is our IT strategy: to help bp transform into an integrated energy company,” Fotiou says. “We try to focus on priorities that make sense for different areas of the company—always with a customer-centric outlook. Every area has their

By Rae Ann Varona ( January 28, 2025, 10:29 PM EST) — Two federal employees lodged a putative class action against the Office of Personnel Management in Washington, D.C., federal court challenging a new centralized messaging system, citing an online claim that agencies were instructed to send worker information to the OPM’s new chief of staff, a former Elon Musk employee…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Galileo, a San Francisco-based startup, is betting that the future of artificial intelligence depends on trust. Today, the company launched a new product, Agentic Evaluations, to address a growing challenge in the world of AI: making sure the increasingly complex systems known as AI agents actually work as intended. AI agents — autonomous systems that perform multi-step tasks like generating reports or analyzing customer data — are gaining traction across industries. But their rapid adoption raises a crucial question: How can companies verify these systems remain reliable after deployment? Galileo’s CEO, Vikram Chatterji, believes his company has found the answer. “Over the last six to eight months, we started to see some of our customers trying to adopt agentic systems,” said Chatterji in an interview. “Now LLMs can be used as a smart router to pick and choose the right API calls towards actually completing a task. Going from just generating text to actually completing a task was a very big chasm that was unlocked.” A diagram showing how Galileo evaluates AI agents at three key stages: tool selection, error detection and task completion. (Credit: Galileo) AI agents show promise, but enterprises demand accountability Major enterprises like Cisco and Ema (the latter founded by Coinbase’s former chief product officer) have already adopted Galileo’s platform. These companies use AI agents to automate tasks from customer support to financial analysis, and report significant productivity gains. “A sales representative who’s trying to do outreach and outbounds would otherwise use maybe a week of their time to do that, versus with some of these AI-enabled agents, they’re doing that within two days or less,” Chatterji explained, highlighting the return on investment for enterprises. Galileo’s new framework evaluates tool selection quality, detects errors in tool calls, and tracks overall session success. It also monitors essential metrics for large-scale AI deployment, including costs and latency. A dashboard showing how Galileo evaluates AI agents at three key stages: tool selection, error detection and task completion. (Credit: Galileo) $68 million in funding fuels Galileo’s push into enterprise AI The launch builds on Galileo’s recent momentum. The company raised $45 million in series B funding led by Scale Venture Partners last October, bringing its total funding to $68 million. Industry analysts project the market for AI operations tools could reach $4 billion by 2025. The stakes are high as AI deployment accelerates. Studies show even advanced models like GPT-4 can hallucinate about 23% of the time during basic question-and-answer tasks. Galileo’s tools help enterprises identify these issues before they impact operations. “Before we launch this thing, we really, really need to know that this thing works,” Chatterji said, describing customer concerns. “The bar is really high. So that’s where we gave them this tool chain, such that they could just use our metrics as the basis for these tests.” Addressing AI hallucinations and enterprise-scale challenges The company’s focus on reliable, production-ready solutions positions it well in a market increasingly concerned with AI safety. For technical leaders deploying enterprise AI, Galileo’s platform provides essential guardrails for ensuring AI agents perform as intended while controlling costs. As enterprises expand their use of AI agents, performance monitoring tools become crucial infrastructure. Galileo’s latest offering aims to help businesses deploy AI responsibly and effectively at scale. “2025 will be the year of agents. It is going to be very prolific,” Chatterji noted. “However, what we’ve also seen is a lot of companies that are just launching these agents without good testing is leading to negative implications…The need for proper testing and evaluations is more than ever before.” source

By Christopher Cole ( January 30, 2025, 6:42 PM EST) — The newly installed head of the Federal Communications Commission says he plans to investigate whether local NPR and PBS stations are using underwriting spots to air commercial advertising…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has released a new proprietary AI model in time to counter the rapid rise of open source rival DeepSeek R1 — but will it be enough to blunt the latter’s success? Today, after several days of rumors and increasing anticipation among AI users on social media, OpenAl is debuting o3-mini, the second model in its new family of “reasoners,” Al models that take slightly more time to “think,” analyze their own processes and reflect on their own “chains of thought” before responding to user queries and inputs with new outputs. The result is a model that can perform at the level of a PhD student or even degree holder on answering hard questions in math, science, engineering and many other fields. The o3-mini model is now available on ChatGPT, including the free tier, and OpenAI’s application programming interface (API), and it’s actually less expensive, faster, and more performant than the previous high-end model, OpenAI’s o1 and its faster, lower-parameter count sibling, o1-mini. While inevitably it will be compared to DeepSeek R1, and the release date seen as a reaction, it’s important to remember that o3 and o3-mini were announced well prior to the January release of DeepSeek R1, in December 2024 — and that OpenAI CEO Sam Altman stated previously on X that due to feedback from developers and researchers, it would be coming to ChatGPT and the OpenAI API at the same time. Unlike DeepSeek R1, o3-mini will not be made available as an open source model — meaning the code cannot be taken and downloaded for offline usage, nor customized to the same extent, which may limit its appeal compared to DeepSeek R1 for some applications. OpenAI did not provide any further details about the (presumed) larger o3 model announced back in December alongside o3-mini. At that time, OpenAI’s opt-in dropdown form for testing o3 stated that it would undergo a “delay of multiple weeks” before third-parties could test it. Performance and Features Similar to o1, OpenAI o3-mini is optimized for reasoning in math, coding, and science. Its performance is comparable to OpenAI o1 when using medium reasoning effort, but offers the following advantages: 24% faster response times compared to o1-mini (OpenAI didn’t provide a specific number here, but looking at third-party evaluation group Artificial Analysis’s tests, o1-mini’s response time is 12.8 seconds to receive and output 100 tokens. So for o3-mini, a 24% speed bump would drop the response time down to 10.32 seconds.) Improved accuracy, with external testers preferring o3-mini’s responses 56% of the time. 39% fewer major errors on complex real-world questions. Better performance in coding and STEM tasks, particularly when using high reasoning effort. Three reasoning effort levels (low, medium, and high), allowing users and developers to balance accuracy and speed. It also boasts impressive benchmarks, even outpacing o1 in some cases, according to the o3-mini System Card OpenAI released online (and which was published earlier than the official model availability announcement). o3-mini’s context window — the number of combined tokens it can input/output in a single interaction — is 200,000, with a maximum of 100,000 in each output. That’s the same as the full o1 model and outperforms DeepSeek R1’s context window of around 128,000/130,000 tokens. But it is far below Google Gemini 2.0 Flash Thinking’s new context window of up to 1 million tokens. While o3-mini focuses on reasoning capabilities, it doesn’t have vision capabilities yet. Developers and users looking to upload images and files should keep using o1 in the meantime. The competition heats up The arrival of o3-mini marks the first time OpenAI is making a reasoning model available to free ChatGPT users. The prior o1 model family was only available to paying subscribers of the ChatGPT Plus, Pro and other plans, as well as via OpenAI’s paid application programming interface. As it did with large language model (LLM)-powered chatbots via the launch of ChatGPT in November 2022, OpenAI essentially created the entire category of reasoning models back in September 2024 when it first unveiled o1, a new class of models with a new training regime and architecture. But OpenAI, in keeping with its recent history, did not make o1 open source, contrary to its name and original founding mission. Instead, it kept the model’s code proprietary. And over the last two weeks, o1 has been overshadowed by Chinese AI startup DeepSeek, which launched R1, a rival, highly efficient, largely open-source reasoning model freely available to take, retrain, and customize by anyone around the world, as well as use for free on DeepSeek’s website and mobile app — a model reportedly trained at a fraction of the cost of o1 and other LLMs from top labs. DeepSeek R1’s permissive MIT Licensing terms, free app/website for consumers, and decision to make R1’s codebase freely available to take and modify has led it to a veritable explosion of usage both in the consumer and enterprise markets — even OpenAI investor Microsoft and Anthropic backer Amazon rushing to add variants of it to their cloud marketplaces. Perplexity, the AI search company, also quickly added a variant of it for users. DeepSeek also dethroned the ChatGPT iOS app for the number one place in the U.S. Apple App Store, and is notable for outpacing OpenAI by connecting its R1 model to web search in its app and on the web, something that OpenAI has not yet done for o1, leading to further techno anxiety among tech workers and others online that China is catching up or has outpaced the U.S. in AI innovation — even technology more generally. Many AI researchers and scientists and top VCs such as Marc Andreessen, however, have welcomed the rise of DeepSeek and its open sourcing in particular as a tide that lifts all boats in the AI field, increasing the intelligence available to everyone while reducing costs. Availability in ChatGPT o3 is now rolling out globally to ChatGPT

By Tom Zanki ( January 31, 2025, 12:43 PM EST) — Shares of obesity-focused drug developer Metsera and kidney disease-focused Maze Therapeutics began trading Friday after the companies raised $415 million combined through initial public offerings, guided by four law firms, fueling an uptick of biotechnology-related IPOs…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

DeepSeek’s open-source model, DeepThink (R1), by a little-known company in China, sent shock waves across the technology world. It’s amazing. Yes, it does excel at benchmarks comparable to other state-of-the-art models. Yes, it’s partially open source. Yes, the DeepSeek app explains its reasoning by default. But there are far-reaching implications to this important AI development, especially for privacy, security and geopolitical barriers. The Cost Barrier To Training Models Efficiently Just Plummeted What’s disruptive and truly amazing is how the DeepSeek engineers created the DeepThink (R1) model, especially the cost to train the model. Due to clever optimizations, the DeepThink (R1) model purportedly cost around $5.5 million to train. That’s tens of millions of dollars less than comparable models. We expect these optimizations to be copied and improved upon by model builders worldwide. Short term, that is bad news for NVIDIA because it will temper the demand. Longer term, however, the lower cost (and, thus, energy) will open up model creation opportunities for many, many more startups and enterprises alike, thereby increasing demand. This validates the fact that vendors that only provide core AI foundation models won’t be enough, and this disruptive shift will open up the AI model market even more. For tech leaders, this should be a strong signal to closely examine overreliance on a few big players in the AI space. Also, don’t forget that while the cost to train the model has just declined significantly, the cost to support inferencing will still require significant compute (and storage). Don’t cry for NVIDIA and the hyperscalers just yet. Also, there might be an opportunity for Intel to claw its way back to relevance. Intel ceded dominance of high-end computing to NVIDIA, but the company has always bet that tech leaders will want to embed AI everywhere, from the PC to the edge to the data center to the cloud, and there will be strong demand for smaller, targeted large language models (LLMs) — a portfolio of chips at the appropriate price point might just pay off. Edge Computing And Intelligence Is No Longer An Aspiration — It’s Here The DeepSeek app already has millions of downloads on mobile phone app stores. The app connects to and uses the model in the cloud. Another cool way to use DeepSeek, however, is to download the model to any laptop. Several Forrester analysts have run tests on laptops. It’s a bit slow but runnable. This means that the models can run far and wide without the need for specialized hardware. This will dramatically accelerate edge computing. Edge computing processes data closer to its source, reducing latency and bandwidth usage. This helps firms anticipate customer needs, act on their behalf, and operate businesses efficiently in localized contexts, including internet-of-things-enabled scenarios. The ability to run LLMs on laptops and edge devices amplifies these benefits by providing powerful AI capabilities directly at the edge. Based on what we’ve seen so far from DeepSeek R1, it can process and analyze vast amounts of data in real time, enabling more responsive and intelligent edge devices. This capability is particularly valuable in scenarios where immediate decision-making is critical, such as in autonomous vehicles, industrial automation, and smart cities. By leveraging LLMs at the edge, enterprises can achieve faster data processing, improved accuracy in predictions, and enhanced user experiences, all strategic goals of AIOps initiatives. Geopolitical, Privacy, And Security Barriers Remain The massive downloads of DeepSeek mean that thousands (and even millions of users) are experimenting and uploading what could be sensitive information into the app. This may include enterprise data, especially for developers experimenting with the technology. According to its privacy policy, DeepSeek explicitly says it can collect “your text or audio input, prompt, uploaded files, feedback, chat history, or other content” and use it for training purposes. It also states that it can share this information with law enforcement agencies, public authorities, etc., at its discretion. Educate and inform your employees on the ramifications of using this technology and inputting personal and company information into it. Align with product leaders on whether developers should be experimenting with it and whether the product should support its implementation without stricter privacy requirements. There Is No Excuse Not To Pursue AI Innovation (And ROI) Anymore DeepSeek is not just “China’s ChatGPT”; it is a giant leap for global AI innovation, because by reducing the cost, time, and energy to build models, many more researchers and developers can experiment, innovate, and try new sets. Having said that, one should not assume that LLMs are the only path to more sophisticated AI. It may be that a new model architecture brings us right back to needing gobs of compute, especially for artificial general intelligence. But for the time being, DeepSeek’s release of this model and the techniques it used to create it should be a celebratory moment for AI. Now is not the time to scale back on AI prematurely. source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The release of the DeepSeek-R1 reasoning model has caused shockwaves across the tech industry, with the most obvious sign being the sudden selloff of major AI stocks. The advantage of well-funded AI labs such as OpenAI and Anthropic no longer seems very solid, as DeepSeek has reportedly been able to develop its o1 competitor at a fraction of the cost. While some AI labs are currently in crisis mode, as far as the enterprise sector is concerned, it’s mostly good news.  Cheaper applications, more applications As we had said here before, one of the trends worth watching in 2025 is the continued drop in the cost of using AI models. Enterprises should experiment and build prototypes with the latest AI models regardless of the price, knowing that continued price reduction will enable them to eventually deploy their applications at scale.  That trendline just saw a huge step change. OpenAI o1 costs $60 per million output tokens versus $2.19 per million for DeepSeek-R1. And, if you’re concerned about sending your data to Chinese servers, you can access R1 on U.S.-based providers such as Together.ai and Fireworks AI, where it is priced at $8 and $9 per million tokens, respectively — still a huge bargain in comparison to o1.  To be fair, o1 still has the edge over R1, but not so much as to justify such a huge price difference. Moreover, the capabilities of R1 will be sufficient for most enterprise applications. And, we can expect more advanced and capable models to be released in the coming months. We can also expect second-order effects on the overall AI market. For instance, OpenAI CEO Sam Altman announced that free ChatGPT users will soon have access to o3-mini. Although he did not explicitly mention R1 as the reason, the fact that the announcement was made shortly after R1 was released is telling. More innovation R1 still leaves a lot of questions unanswered — for example, there are multiple reports that DeepSeek trained the model on outputs from OpenAI large language models (LLMs). But if its paper and technical report are correct, DeepSeek was able to create a model that nearly matches the state of the art while slashing costs and removing some of the technical steps that require a lot of manual labor. If others can reproduce DeepSeek’s results, it can be good news for AI labs and companies that were sidelined by the financial barriers to innovation in the field. Enterprises can expect faster innovation and more AI products to power their applications. What will happen to the billions of dollars that big tech companies have spent on acquiring hardware accelerators? We still haven’t reached the ceiling of what is possible with AI, so leading tech companies will be able to do more with their resources. More affordable AI will, in fact, increase demand in the medium to long term. But more importantly, R1 is proof that not everything is tied to bigger compute clusters and datasets. With the right engineering chops and good talent, you will be able to push the limits of what is possible.  Open source for the win To be clear, R1 is not fully open-source, as DeepSeek has only released the weights, but not the code or full details of the training data. Nonetheless, it is a big win for the open source community. Since the release of DeepSeek-R1, more than 500 derivatives have been published on Hugging Face, and the model has been downloaded millions of times. It will also give enterprises more flexibility over where to run their models. Aside from the full 671-billion-parameter model, there are distilled versions of R1, ranging from 1.5 billion to 70 billion parameters, enabling companies to run the model on a variety of hardware. Moreover, unlike o1, R1 reveals its full thought chain, giving developers a better understanding of the model’s behavior and the ability to steer it in the desired direction. With open source catching up to closed models, we can hope for a renewal of the commitment to share knowledge and research so that everyone can benefit from advances in AI. source

By Abdi Shayesteh ( January 29, 2025, 4:57 PM EST) — During law school, law students get a taste of learning-by-doing through mock trials, either in class[1] or through school competitions.[2] Indeed, in the next few months, the American Mock Trial Association will hold its opening round championship series tournament, culminating in its national championship…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source