LLMs

“Maximizing Efficiency: A Look at Thoras.ai’s Automated Resource Allocation for Kubernetes Workloads”

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“Thoras essentially integrates alongside a cloud-based service and it consistently monitors the usage of that service,” company CEO Nilo Rahmani told TechCrunch. They launched the company right after the first of the year and closed their pre-seed funding just a few weeks ago. In terms of AI, the company currently uses more task-based machine learning than generative AI and large language models (LLMs). “A lot of the problems that we’re facing are systemic issues, and there are a lot of numbers involved. They see LLMs being more useful in troubleshooting after the fact at some point as they fill out the product.

Utilizing Advanced Linguistic Models for Autonomous Error Recovery in Home Robotics

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Even when some or all of those are addressed, there remains the question of what happens when a system makes an inevitable mistake. We can’t, however, expect consumers to learn to program or hire someone who can help any time an issue arrives. Thankfully, this is a great use case for LLMs (large language models) in the robotics space, as exemplified by new research from MIT. “LLMs have a way to tell you how to do each step of a task, in natural language. It’s a simple, repeatable task for humans, but for robots, it’s a combination of various small tasks.

“Streamlining Business Intelligence Tools for Increased Efficiency: How LLMs are Revolutionizing Data Management”

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At the moment, large organizations often employ “business intelligence” (BI) tools to figure out what the heck is going on inside their operations. Essentially, BI tools connect to a business database and use SQL to create visualizations and build out BI dashboards. There are huge companies involved in this space: Tableau (owned by Salesforce), Power BI (owned by Microsoft), Looker (owned by Google), and QuickSight (owned by Amazon) to name just a handful. And how is this marketing campaign performing.” He said other players in the market target data users, whereas Fluent targets the business market, not data. For example, Metabase is an open-source analytics and business intelligence application that allows users to create dashboards more easily.

“Inside the World of LLM Building in China: Insights from an Alibaba Employee”

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Chinese tech companies are gathering all sorts of resources and talent to narrow their gap with OpenAI, and experiences for researchers on both sides of the Pacific Ocean can be surprisingly similar. The parallel glimpse into their typical day reveals striking similarities, with wake-up times at 9 a.m. and bedtime around 1 a.m. Both start the day with meetings, followed by a period of coding, model training and brainstorming with colleagues. Besides building its own LLM in-house, Alibaba has been aggressively investing in startups such as Moonshot AI, Zhipu AI, Baichuan and 01.AI. Facing competition, Alibaba has been trying to carve out a niche, and its multilingual move could become a selling point.

“Latest Updates: Google Debuts Fresh Open LLMs, Rivian Enacts Employee Cutbacks, and Signal Introduces Usernames”

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Google releases new open LLMs, Rivian lays off staff and Signal rolls out usernamesWelcome, folks, to Week in Review (WiR), TechCrunch’s regular newsletter covering noteworthy happenings in the tech industry. This week, Google launched two new open large language models, Gemma 2B and Gemma 7B, in its continued bid for generative AI dominance. The company, which describes the LLMs as “inspired by Gemini,” its flagship family of GenAI models, made each available for commercial and research usage. Change Healthcare hit: Change Healthcare, one of the largest healthcare tech companies in the U.S., confirmed that a cyberattack on its systems occurred recently. YouTube triumphant: YouTube dominates TV streaming in the U.S., per Nielsen’s latest report.

“Introducing: Google’s Latest Additions – Two New Open LLM Programs”

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Barely a week after launching the latest iteration of its Gemini models, Google today announced the launch of Gemma, a new family of lightweight open-weight models. To get started with Gemma, developers can get access to ready-to-use Colab and Kaggle notebooks, as well as integrations with Hugging Face, MaxText and Nvidia’s NeMo. While Google highlights that these are open models, it’s worth noting that they are not open-source. Indeed, in a press briefing ahead of today’s announcement, Google’s Janine Banks stressed the company’s commitment to open source but also noted that Google is very intentional about how it refers to the Gemma models. “[Open models] has become pretty pervasive now in the industry,” Banks said.

How a Conversation-Driven AI Strategy Helped a Startup Secure $8 Million from Regulated Industries

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As conversational AI begins to take over the world, chatbots are being given a new lease of life. Parcel delivery giant DPD recently had to disable part of its online support chatbot after it swore at a customer. The demand for conversational AI is skyrocketing, and is set to explode to a mind-boggling $38 billion globally by 2029. However, regulated sectors are still grappling with Natural Language Understanding (NLU) and Large Language Models (LLMs). While an LLM might be able to sound like a human and understand context, regulated industries need high guard rails on an AI-driven approach.

“Maximizing Start-Up Success: 5 Key Strategies for Effective LLM Deployment”

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In fact, an April 2023 Arize survey found that 53% of respondents planned to deploy LLMs within the next year or sooner. The H100 GPU from Nvidia, a popular choice for LLMs, has been selling on the secondary market for about $40,000 per chip. One source estimated it would take roughly 6,000 chips to train an LLM comparable to ChatGPT-3.5. That source estimated that the power consumption to run ChatGPT-3.5 is about 1 GWh a day, or the combined daily energy usage of 33,000 households. Power consumption can also be a potential pitfall for user experience when running LLMs on portable devices.

Assisting Businesses with Offline Deployment of LLMs: Giga ML’s Solution

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In search of one, they founded Giga ML, a startup building a platform that lets companies deploy LLMs on-premise — ostensibly cutting costs and preserving privacy in the process. “Giga ML addresses both of these challenges.”Giga ML offers its own set of LLMs, the “X1 series,” for tasks like generating code and answering common customer questions (e.g. But it’s tough to say how X1 compares qualitatively; this reporter tried Giga ML’s online demo but ran into technical issues. Even if Giga ML’s models are superior in some aspects, though, can they really make a splash in the ocean of open source, offline LLMs? “Giga ML’s mission is to help enterprises safely and efficiently deploy LLMs on their own on-premises infrastructure or virtual private cloud,” Vummadi said.

The Crucial Need to Control AI in the Multi-Trillion-Dollar API Market

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Application programming interfaces (APIs) power the modern internet, including most websites, mobile apps, and IoT devices we use. This phenomenon, often referred to as the “API economy,” is projected to have a total market value of $14.2 trillion by 2027. How AI integration changed the API landscapeVarious kinds of AI have been here for a while, but it’s generative AI (and LLMs) that completely changed the risk landscape. Many AI companies use the benefits of API technologies to bring their products to every home and workplace. Various kinds of AI have been here for a while, but it’s generative AI (and large language models [LLMs]) that completely changed the risk landscape.