Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. In enterprise security, speed is everything. The quicker an analyst can pinpoint legitimate threat signals, the faster they can identify whether there's a breach, and how to respond. As generative AI solutions like GPT develop, human analysts have the potential to supercharge their decision making. Today, cyber intelligence provider Recorded Future announced the release of what it claims is the first AI for threat intelligence.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Millions of users have flocked to ChatGPT since its mainstream launch in November 2022. Thanks to its exceptional human-like language generation capabilities, its aptitude for coding software, and its lightning-fast text analysis, ChatGPT has quickly emerged as a go-to tool for developers, researchers and everyday users. But as with any disruptive technology, generative AI systems like ChatGPT come with potential risks. In particular, major players in the tech industry, state intelligence agencies and other governmental bodies have all raised red flags about sensitive information being fed into AI systems like ChatGPT. Don't miss our newest special issue: Data centers in 2023: How to do more with less.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Coursera is offering more classes and degrees so that global labor market won't be blindsided by the rise of generative AI and remote work. As businesses adopt generative AI to improve customer offerings and productivity, it will also create an unprecedented demand for reskilling – with up to 49% of workers having half or more of their tasks exposed to large language models. "Today, we're excited to announce several new content offerings, ChatGPT-powered platform innovations, and expanded immersive learning experiences to better serve our learners and educators worldwide," said Jeff Maggioncalda, CEO of Coursera, in a blog post. To meet the growing demand for AI skills in the workforce, Coursera is increasing its selection of AI-related courses, including a ChatGPT Teach-Out (University of Michigan) and AI for Good Specialization (DeepLearning.AI).

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Less than a decade ago, the prevailing wisdom was that every business should undergo digital transformations to boost internal operations and improve client relationships. Next, they were being told that cloud workloads are the future and that elastic computer solutions enabled them to operate in an agile and more cost-effective manner, scaling up and down as needed. While digital transformations and cloud migrations are undoubtedly smart decisions that all organizations should make (and those that haven't yet, what are you doing!), security systems meant to protect such IT infrastructures haven't been able to keep pace with threats capable of undermining them. As internal business operations become increasingly digitized, boatloads more data are being produced.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Poe, a platform for creating and interacting with chatbots powered by large language models (LLMs), has launched a new feature that lets anyone create their own chatbot with just a few words. The feature was quietly rolled out on Friday to all Poe users, allowing anyone to write a short text prompt and choose an existing LLM as a base for their new creation. The base can be either Claude Instant or ChatGPT, both of which are leading general-purpose conversational agents. The user-generated prompt and the base LLM are then used to generate a new chatbot that can be customized and shared with others.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. The open source technology movement has been having a moment over the past few weeks thanks to AI -- following a wave of recent large language model (LLM) releases and an effort by startups, collectives and academics to push back on the shift in AI to closed, proprietary LLMs. State-of-the-art LLMs require huge compute budgets – OpenAI reportedly used 10,000 Nvidia GPUs to train ChatGPT– and deep ML expertise, so few organizations can train them from scratch. Yet, increasingly, those that have the resources and expertise are not opening up their models -- the data, source code, or deep learning's secret sauce, the model weights -- to public scrutiny, relying on API distribution instead. That is where open source AI is stepping into the void to democratize access to LLMs.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. As the whole world knows, the field of artificial intelligence (AI) is progressing at breakneck speeds. Companies big and small are racing to implement the power of generative AI in new and useful ways. I am a firm believer in the value of AI to advance human productivity and solve human problems, but I am also quite concerned about the unexpected consequences. As I told the San Francisco Examiner last week, I signed the controversial AI "Pause Letter" along with thousands of other researchers to draw attention to the risks associated with large-scale generative AI and help the public understand that the risks are currently evolving faster than the efforts to contain them.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Two prominent figures in the artificial intelligence industry, Yann LeCun, the chief AI scientist at Meta, and Andrew Ng, the founder of Deeplearning.AI, argued against a proposed pause on the development of powerful AI systems in an online discussion on Friday. The discussion, titled "Why the 6-Month AI Pause Is a Bad Idea," was hosted on YouTube and drew thousands of viewers. During the event, LeCun and Ng challenged an open letter that was signed by hundreds of artificial intelligence experts, tech entrepreneurs and scientists last month, calling for a moratorium of at least six months on the training of AI systems more advanced than GPT-4, a text-generating program that can produce realistic and coherent replies to almost any question or topic. "We have thought at length about this six-month moratorium proposal and felt it was an important enough topic -- I think it would actually cause significant harm if the government implemented it -- that Yann and I felt like we wanted to chat with you here about it today," Mr. Ng said in his opening remarks.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. The sprint to develop LinkedIn's recently released generative AI tools took only three months, Ya Xu, VP of engineering and head of data and artificial intelligence (AI), told VentureBeat in an interview. The timeline, she said, was "unprecedented" for a large company like LinkedIn, given the many changes engineering and product teams implemented based on OpenAI's latest GPT models, including ChatGPT and GPT-4, as well as some open-source models. These include generative AI-powered collaborative articles, job descriptions and personalized writing suggestions for LinkedIn profiles. For example, she explained, her teams were able in just one month to generate job descriptions automatically and serve live traffic.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Databricks, a company specializing in data lakehouse technology, announced on Tuesday a new platform designed for the manufacturing industry. Called lakehouse for manufacturing, the platform aims to unify data and artificial intelligence (AI) for various analytics use cases such as predictive maintenance, quality control and supply chain optimization. The platform builds on Databricks' core data lakehouse platform, which leverages Delta Lake, Apache Spark and MLFlow, open-source projects that enable scalable data processing and machine learning (ML) workflows. The platform also integrates with model serving, a service that Databricks introduced last month to simplify the deployment and management of ML models in production.