Google has been warned by one of its engineers that the company is not in a position to win the artificial intelligence race and could lose out to commonly available AI technology. A document from a Google engineer leaked online said the company had done "a lot of looking over our shoulders at OpenAI", referring to the developer of the ChatGPT chatbot. However, the worker, identified by Bloomberg as a senior software engineer, wrote that neither company was in a winning position. "The uncomfortable truth is, we aren't positioned to win this arms race and neither is OpenAI. While we've been squabbling, a third faction has been quietly eating our lunch," the engineer wrote.

Open-source technologies have had a profound impact on the world of AI and machine learning, enabling developers, data scientists, and organizations to collaborate, innovate, and build better AI solutions. As large AI models like GPT-3.5 and DALL-E become more prevalent, organizations are also exploring ways to leverage existing open-source models and tools without needing to put a tremendous amount of effort into building them from scratch. Microsoft Azure AI is leading this effort by working closely with GitHub and data science communities, and providing organizations with access to a rich set of open-source technologies for building and deploying cutting-edge AI solutions. At Azure Open Source Day, we highlighted Microsoft's commitment to open source and how to build intelligent apps faster and with more flexibility using the latest open-source technologies that are available in Azure AI. Recent advancements in AI propelled the rise of large foundation models that are trained on a vast quantity of data and can be easily adapted to a wide variety of applications across various industries.

Between Facebook's core social network product and apps like Messenger and WhatsApp, Mark Zuckerberg's tech giant has undeniably changed the way people communicate. Could it next help change the way we communicate with robots? In 2021, the idea of being able to communicate with artificial intelligence through natural language is nowhere near as science fiction as it once was. Whether it's Amazon's Echo voice assistant or the voice bots we interact with when you phone your bank, A.I. today means that machines can do a pretty good job of understanding what humans are asking for, without the human in question having to do much to modify the way that they're speaking. Partly because most robots are still used in industrial or lab-based settings, where there's not quite the same requirement for them to be accessible to everyday users, robot interactions remain more opaque in their operation.

Quebec Artificial Intelligence Institute (Mila) and IBM have teamed up to accelerate artificial intelligence (AI) and machine learning research using open-source technology. Mila and IBM have been collaborating since early 2020 on a project that is meant to make a key component of AI, known as hyperparameter optimization, more accessible. The organizations claim that this would improve machine learning model performances and pinpoint within the'black box' of AI where models need work. "A collaboration with…IBM is a great opportunity to accelerate the development of an open-source solution…initiated at Mila." – Yoshua Bengio, Mila The two organizations are looking to integrate the Quebec institute's open-source software, Oríon, with IBM's Watson Machine Learning Accelerator, an AI model training and inference tool that the tech giant offers to businesses. The overall goal, they claim, is to "improve the development, deployment, and ongoing management of complex AI and deep learning models, as well as to make tools more accessible to a larger base of scientists, engineers, and developers through automation."

There are many open-source projects in Artificial Intelligence that are never heard of. But many of these projects also grow to be part of the fundamentals in Artificial Intelligence. Everybody has heard about TensorFlow in the AI world! But it was initially just a project by the Google Brain team for internal Google use. Similarly, most of these open-source projects start as passion projects of developers in universities or tech companies like Google, Microsoft, etc.

Cloudera said today as part of its second-quarter earnings report that it is acquiring Fast Forward Labs, a startup that gives companies the latest information on how to apply machine learning and AI to their businesses, as well as consulting. The company's stock has weathered somewhat of a beating since it went public, though it was able to beat Wall Street's expectations today. But the more interesting news is the acquisition of Fast Forward Labs, a company that specializes in consulting with larger enterprises about emerging trends in machine learning that can help their businesses grow. Cloudera specializes in operating on top of open-source technology, looking to deliver an enterprise-grade product for larger organizations. "On the way, we built a profitable company with real impact on our clients' products and businesses. I'm proud of what we've accomplished," CEO and co-founder Hilary Mason said in a post announcing the acquisition.

When millions of people across the world tuned in to watch DeepMind's machine beat the human Go world champion Lee Sedol, they also witnessed a historic victory for open-source. DeepMind used a scientific computing framework called Torch extensively in the development and execution of AlphaGo's neural networks. Torch was first released back in 2002 under a BSD open-source license with algorithms that are still commonly used by data scientists such as multi-layer perceptrons, support vector machines and K-nearest neighbours. Torch also supported ensembles -- a popular technique that combines the output of multiple algorithms, usually with a weighted average. It's not just open-source software that contributed to the growth of machine learning.