Unless you've been living under a rock, you've probably heard the term "generative AI" at least a handful of times now, perhaps thanks to the wildly popular ChatGPT service. The AI-powered chatbot's success didn't just shine a spotlight on OpenAI, the creator behind it, but it also catalyzed an AI arms race in the tech industry – a race from which Apple has been noticeably absent. Earlier this month, Google made a flurry of AI-related announcements at its annual developer conference, including a new AI-infused version of search and Bard, its AI-powered chatbot, which is being rolled out across the world. Before that, Microsoft built generative AI into its suite of long-established productivity apps like Word, PowerPoint and Outlook in a move that's changing how more than a billion people work. In February, Meta released its own sophisticated AI model, which has many of the same capabilities at ChatGPT and Bard, as open-source software for public use.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Once again, AI news topped the tech headlines this week -- in particular, the generative AI market is becoming increasingly competitive, with both new and well-established enterprises making significant investments. This includes GitHub's new Copilot X; startup Codium AI's new code-integrity tool TestGPT; and a whole slew of new tools, services and capabilities from Nvidia. Still, skepticism remains, with OpenAI's CEO Sam Altman even expressing apprehension. Not topping the list (but still noteworthy AI news): Databricks released its GPT-like Dolly; OpenAI turned ChatGPT into a platform overnight with several new plugins; OpenAI rival Character AI announced a $1 billion valuation; and Google released Bard, a competitor to ChatGPT, Claude and Bing Chat.

The downfall of Silicon Valley bank affects the entire technology industry. This week, we chat with Alex Wilhelm, editor in chief at TechCrunch, about why SVB failed and what it means for the broader ecosystem. And why were VCs so quick to abandon it? Like we've said before, the AI news will never end! Listen below or subscribe on your podcast app of choice.

Artificial Intelligence (AI) has transformed the way we live, work, and interact with technology. From virtual assistants like Siri and Alexa to self-driving cars and image recognition systems, AI is quickly becoming a ubiquitous technology that touches every aspect of our daily lives. With so many AI tools and resources available, it can be challenging to keep up with the latest trends and find the most relevant tools for your needs. To help you stay on top of the game, I've compiled a list of the top directories to discover new AI tools. Whether you're a developer, data scientist, or just curious about AI, these directories will help you explore and discover the latest AI tools and resources available.

A new method to assess the quality of organs for donation is set to revolutionise the transplant system – and it could help save lives and tens of millions of pounds. The National Institute for Health and Care Research (NIHR) is contributing more than £1 million in funding to develop the new technology, which is known as Organ Quality Assessment (OrQA). It works in the same way as Artificial Intelligence-based facial recognition to evaluate the quality of an organ. It is estimated the technology could result in up to 200 more patients receiving kidney transplants and 100 more receiving liver transplants a year in the UK. Colin Wilson, transplant surgeon at Newcastle upon Tyne Hospitals NHS Foundation Trust and co-lead of the project, said: "Transplantation is the best treatment for patients with organ failure, but unfortunately some organs can't be used due to concerns they won't function properly once transplanted. "The software we have developed'scores' the quality of the organ and aims to support surgeons to assess if the organ is healthy enough to be transplanted.

Informatica, an enterprise cloud data management specialist, has launched the industry's only free cloud data loading, integration and ETL/ELT service – Informatica Cloud Data Integration-Free and PayGo. The new offering targets data practitioners and non-technical users such as in marketing, sales, and revenue operations teams to build data pipelines within minutes. For example, it provides operations teams with a fast, free, and frictionless way to load, integrate and analyze high-quality campaign, pipeline, forecast, and revenue data. In addition, data analysts and data engineers benefit from increased productivity and rapid development. This is the second in a series of releases that began with the Informatica Data Loader launch in May 2022.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. This was AI race week, seemingly, and Google, Nvidia, AT&T, Siemens and your local retailer were all there. At the pole position, Google held an AI event on Wednesday. In addition to announcing the company's recent advances in AI technology, CEO Sundar Pichai made the case for responsible AI, saying, "We see so much opportunity ahead and are committed to making sure the technology is built in service of helping people, like any transformational technology." Nvidia has entered the speech AI race (currently dominated by Google and Meta) by partnering with Mozilla Common Voice.

The road to maturity for any technology in the enterprise is long and arduous. Take data and analytics platforms as an example. Data from 451 Research's Voice of the Enterprise series in March found a third of companies surveyed were still yet to fully embrace a data-driven approach to strategic decision making. If that is the case, whither artificial intelligence? Writing for Enterprise Talk earlier this month, Swapnil Mishra notes of businesses still being in the'AI adolescence' phase, citing research from Accenture which found 63% of 1,200 companies polled were still experimenting with projects.

There's a good chance the answer that an employee needs to do their job effectively is available within the company, but it can often be difficult to surface. Research suggests that around three hours per day are spent searching for information. Even more frustratingly, around 44 percent of all searches end in failure. In total, around $1 million per month is lost for every 1,000 employees in a business. Starmind believes that it doesn't have to be this way and is using AI to power a customisable real-time knowledge network that gives teams on-demand access to the answers they need.

Open AI has trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using just a small amount of labeled contractor data. With a bit of fine-tuning, the AI research and deployment company is confident that its model can learn to craft diamond tools, a task that usually takes proficient humans over 20 minutes (24,000 actions). Its model uses the native human interface of keypresses and mouse movements, making it quite general, and represents a step towards general computer-using agents. A spokesperson for the Microsoft-backed firm said: "The internet contains an enormous amount of publicly available videos that we can learn from. You can watch a person make a gorgeous presentation, a digital artist draw a beautiful sunset, and a Minecraft player build an intricate house. However, these videos only provide a record of what happened but not precisely how it was achieved, i.e. you will not know the exact sequence of mouse movements and keys pressed. "If we would like to build large-scale foundation models in these domains as we've done in language with GPT, this lack of action labels poses a new challenge not present in the language domain, where "action labels" are simply the next words in a sentence." In order to utilise the wealth of unlabeled video data available on the internet, Open AI introduces a novel, yet simple, semi-supervised imitation learning method: Video PreTraining (VPT). The team begin by gathering a small dataset from contractors where it records not only their video, but also the actions they took, which in its case are keypresses and mouse movements. With this data the company can train an inverse dynamics model (IDM), which predicts the action being taken at each step in the video. Importantly, the IDM can use past and future information to guess the action at each step. The spokesperson added: "This task is much easier and thus requires far less data than the behavioral cloning task of predicting actions given past video frames only, which requires inferring what the person wants to do and how to accomplish it.