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Enterprises continued to accelerate the adoption of AI and machine learning to solve product and business challenges and improve revenues in 2021. Meanwhile, AI startups have experienced significant growth, roping in major investments to improve their product offerings and meet the growing demand for AI solutions across sectors. In fact, data from CB Insights Research shows that while the number of equity funding deals in the global AI space this year is just slightly less than the last (2,384 deals in 2021 versus 2,450 in 2020), the amount of capital invested has almost doubled to $68 billion. As we head into 2022, here's a quick look back at the milestones that shaped the AI space over the past 12 months. To start the year, OpenAI announced DALL-E, a multimodal AI system that generated images from text.

Microsoft invests $1 billion in OpenAI to develop AI technologies on Azure


Microsoft today announced that it would invest $1 billion in OpenAI, the San Francisco-based AI research firm cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, Elon Musk, and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman. In a blog post, Brockman said the investment will support the development of artificial general intelligence (AGI) -- AI with the capacity to learn any intellectual task that a human can -- with "widely distributed" economic benefits. To this end, OpenAI intends to partner with Microsoft to jointly develop new AI technologies for the Seattle company's Azure cloud platform and will enter into an exclusivity agreement with Microsoft to "further extend" large-scale AI capabilities that "deliver on the promise of AGI." Additionally, OpenAI will license some of its technologies to Microsoft, which will commercialize them and sell them to as-yet-unnamed partners, and OpenAI will train and run AI models on Azure as it works to develop new supercomputing hardware while "adhering to principles on ethics and trust." "AI is one of the most transformative technologies of our time and has the potential to help solve many of our world's most pressing challenges," said Microsoft CEO Satya Nadella.

The top AI news of 2021


The year has introduced some gigantic AI models and GPT-3 competitors while witnessing regulatory crackdowns on big tech from countries worldwide. Some companies provided huge aids for the pandemic struck nations, and some went in other directions, like flying to space. The year only kept getting more interesting by the end. We've got you a timeline of the year, highlighting the most important updates of 2021 you should know. AI21 Labs released a language model that it claims is'the largest and most sophisticated language model ever released for general use by developers.'

78 AI Companies Around The World That Are Unicorns Today


AI is at the heart of digital disruption and on its way to becoming one of the biggest game-changers in the next few years. Early adopters of AI are reaping significant benefits and have differentiated themselves from the rest. As a result, the AI sector is garnering the attention of numerous investors globally, increasing the number of AI unicorns in just a few years. In India itself, as many as 11 startups earned unicorn tags during the black swan year 2020. This article lists all the AI companies that have reached a valuation of $1 billion or more. A technology platform company, Argo AI, is creating integrated self-driving systems. These are manufactured at scale for safe and reliable deployment in ride-sharing and goods delivery services. Along with Ford and Lyft, Argo AI is planning to launch a self-driving ride-hailing service in the US.

The 2021 machine learning, AI, and data landscape


Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the past year or so. Part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the past year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicles, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use trend cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entirely new categories (data observability, reverse ETL, metrics stores, etc.) appearing or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem -- coauthored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019: Part I and Part II, and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym: Machine learning, Artificial intelligence, and Data (MAD) -- this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone -- a macro view that will hopefully be interesting and approachable to most, and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with a deeper familiarity with the industry. Let's start with a high-level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: Why is this happening? How long can it keep going?