Emerging technologies

Lore & Background

In the history of technology, emerging technologies are contemporary advances and innovation in various fields. Over centuries, innovative methods and new technologies have been developed through theoretical research and commercial research and development. Technological growth includes incremental developments, such as the gradual roll-out of DVD as a follow-on from compact disc technology, and disruptive technologies, where a new method replaces the previous technology, like automobiles replacing horse-drawn carriages.

Reader's Guide

Emerging technologies are significant because they are perceived as capable of changing the status quo and often involve convergence—previously separate technologies moving towards stronger interconnection and similar goals, creating new efficiencies. Debates surround their impact: some writers, including computer scientist Bill Joy, warn that elites could use them for good or evil, potentially causing mass extinction. Advocates see hope for betterment of the human condition, while critics warn of existential risks. Ethical debates center on distributive justice, with some fearing unequal benefits that worsen poverty, and others believing they could eliminate poverty. Analysts like Martin Ford argue that automation from AI and robotics may lead to significant unemployment and increased concentration of wealth. The legacy of emerging technologies is thus marked by both promise and profound uncertainty.

Did You Know?

• Emerging technologies include both new technologies and old technologies finding new applications.
• Converging technologies represent previously distinct fields moving towards stronger inter-connection and similar goals.
• Bill Joy identified clusters of technologies he considered critical to humanity's future.
• Martin Ford argued that robots and automation could result in significant unemployment at all skill levels.

Origins and the Quest for General Intelligence

Founded in November 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman, DeepMind was born from a shared ambition to build artificial intelligence capable of tackling virtually any problem. Hassabis and Legg first crossed paths at the Gatsby Computational Neuroscience Unit at University College London, where their research sensibilities aligned around the idea of general-purpose cognition rather than narrow, task-specific systems. The early team put that philosophy to work by teaching neural networks to play 1970s and 80s Atari titles—Breakout, Pong, Space Invaders—without any hand-coded rules. The systems initially flailed, then gradually discovered strategies, mirroring how a human novice might learn a game from scratch. Backed by investors including Horizons Ventures, Founders Fund, Peter Thiel, Elon Musk, and adviser Jaan Tallinn, the lab attracted attention from multiple tech giants. On January 26, 2014, Google confirmed its acquisition for a reported $400 to $650 million, giving DeepMind the compute and resources to scale its research ambitions.

Conquering the Board and the Arcade

DeepMind's most celebrated public moment arrived in 2016 when AlphaGo defeated Go world champion Lee Sedol in a five-game match, an event later captured in the documentary AlphaGo. But the company's game-playing ambitions stretched far beyond a single board. Its reinforcement-learning architectures, trained on raw pixel input without programmed game knowledge, had already surpassed human performance in Atari classics by 2013. In 2018, researchers extended the approach to the 3D first-person shooter Quake III Arena, and in 2020 the Agent57 system pushed into even more complex territory. The general-purpose philosophy set DeepMind apart from predecessors like IBM's Deep Blue, which was engineered for one specific game. AlphaZero later outperformed the strongest dedicated programs in Go, chess, and shogi after merely days of self-play. More recent entries—MuZero, AlphaStar, AlphaGeometry, FunSearch, AlphaEvolve, AlphaDev, and AlphaTensor—demonstrate how the same learning principles keep expanding into mathematics and algorithm discovery.

From Protein Folding to a Thousand Papers

Beyond games, DeepMind has made profound inroads into scientific problem-solving. In 2020, its AlphaFold system achieved state-of-the-art results on protein-folding benchmark tests, and by July 2022 the company announced the release of over 200 million predicted protein structures—effectively covering virtually every known protein—onto a public database. This work sits alongside a broader research output: as of 2020, the lab had published more than a thousand papers, including thirteen accepted by Nature or Science. The company's media footprint reflected its growing influence; a LexisNexis search found 1,842 news stories referencing DeepMind in 2016 alone, a figure that eased to 1,363 by 2019 as the AlphaGo spotlight faded. The breadth of its scientific contributions, from structural biology to algorithmic discovery, underscores a lab that treats AI as a general-purpose tool for accelerating human knowledge rather than a single-product venture.

Generative AI, Ethics, and a New Chapter

By the early 2020s, DeepMind's portfolio had expanded well past games and science. The lab became responsible for two large language model families—the proprietary Gemini and the open-weight Gemma—alongside generative models such as Imagen for text-to-image, Veo for text-to-video, and Lyria for text-to-music. The pace of the generative-AI race prompted a structural shift: in April 2023, DeepMind merged with Google's Google Brain division to form Google DeepMind, a move partly driven by the public release of ChatGPT and partly the culmination of years of internal tension over autonomy. Ethical governance had been a recurring theme; after the 2014 acquisition, the company established an AI ethics board, later formalized as DeepMind Ethics and Society with philosopher Nick Bostrom as an adviser. The lab also pursued societal applications, signing a 2015 information-sharing agreement with the Royal Free NHS Trust to co-develop a clinical task-management app called Streams. In the summer of 2026, the team relocated into Google's Platform 37 building in King's Cross Central, London.

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