Generative AI (GenAI) is the part of Artificial Intelligence that can generate all kinds of data, including audio, code, images, text, simulations, 3D objects, videos, and so forth. It takes inspiration from existing data, but also generates new and unexpected outputs, breaking new ground in the world of product design, art, and many more. Much of it, thanks to recent breakthroughs in the field like (Chat)GPT and Midjourney.
The World of Generative AI
Exploring the Boundless Possibilities of Generative AI
https://generativeai.net/
ChatGPTにStable Diffusion… 急激な進化を見せるジェネレーティブAIを一括把握【トレンドレポート】
TECHBLITZ編集部
https://techblitz.com/generativeai-report/?fbclid=IwAR29feZaiId0OVUOxDunzuPE8l3T4HAqHvJ6FEXISpS2ysrGcfsJgEh8bOA
ジェネレーティブAIとは
そもそも ジェネレーティブAI（Generative AI、生成/生成的AI）とは、創造的でありながらも現実的な全く新しいデジタル画像や動画、音声、文章やコードなどを作成できる人工知能です。2020年8月のGartnerのハイプサイクルに黎明期の技術として登場していましたが*、2022年夏頃より急激に注目を集めるようになりました。
* https://www.gartner.co.jp/ja/newsroom/press-releases/pr-20200819
既存のAI技術との違いと、今後の普及
これまでAIといえば文字を認識するAIや画像から物体を認識するAIなど、いわゆるディスクリミネーティブ（認識/識別系）AIが主流でしたが、2022年8月頃の画像生成AI「Midjourney」の発表を皮切りに、対話型でテキストを生成する「ChatGPT」や、より質の高い画像が生成できる「Stable Diffusion」など、創造性に優れたジェネレーティブAIが次々と登場し、盛り上がりを見せています。またジェネレーティブAIの中にはオープンソースであるものも多く、2022年後半からその普及に拍車がかかっています。
現在ジェネレーティブAIは研究段階から様々な産業での実用段階に移行しつつあると言えるでしょう。英国金融行為監督機構（FCA）は不正決済の検知を行うAIの開発にジェネレーティブAIを活用しています。
2022年、ジェネレーティブAI分野のスタートアップに対する総投資額は$2.6B（約3,406億円: 2022年平均 1ドル=131円）にものぼりました。*
一方で、あたかも政治家や著名人本人が話しているかのようなディープフェイク動画やフェイクニュースの生成がジェネレーティブAIによってこれまでになく簡単になり、大きな混乱を来たす恐れがあることにも留意すべきです。
* https://www.cbinsights.com/research/generative-ai-funding-top-startups-investors/
Gartner Identifies Five Emerging Trends That Will Drive Technology Innovation for the Next Decade
Social Distancing Technologies, Composable Enterprise, AI-Assisted Design, Differential Privacy and Biodegradable Sensors Among Key Technologies to Watch
Gartner
https://www.gartner.com/en/newsroom/press-releases/2020-08-18-gartner-identifies-five-emerging-trends-that-will-drive-technology-innovation-for-the-next-decade
Five Emerging Technology Trends
Digital me — Technology is becoming increasingly integrated with people to create new opportunities for digital representations of ourselves, such as digital passports and social distancing technologies. Digital twins of humans provide models of individuals that can represent people in both the physical and digital space. The way people interact with the digital world is also moving beyond screens and keyboards to use a combination of interaction modalities (e.g. voice, vision, gesture), and even directly altering our brains.
The technologies to watch include social distancing technologies, health passports, digital twin of the person, citizen twin, multiexperience and 2-Way BMI (brain machine interface).
Composite architectures — The composable enterprise is designed to respond to rapidly changing business needs with packaged business capabilities built upon a flexible data fabric. A composite architecture is implemented with solutions composed of packaged business capabilities. Built-in intelligence is decentralized and extends outward to edge devices and the end user.
To become a more agile organization, the following technologies should be tracked: composable enterprise, packaged business capabilities, data fabric, private 5G, embedded artificial intelligence (AI) and low-cost single-board computers at the edge.
Formative AI — This is a set of emerging AI and related technologies that can dynamically change to respond to situational variances. Some of these technologies are used by application developers and UX designers to create new solutions by using AI enabled tools. Other technologies enable the development of AI models that can evolve dynamically to adapt over time. The most advanced can generate entirely novel models that are targeted to solve specific problems.
Enterprises looking to explore the boundaries of AI should consider AI-assisted design, AI augmented development, ontologies and graphs, small data, composite AI, adaptive ML, self-supervised learning, generative AI and generative adversarial networks.
Algorithmic trust —Trust models based on responsible authorities are being replaced by algorithmic trust models to ensure privacy and security of data, source of assets and identity of individuals and things. Algorithmic trust helps to ensure that organizations will not be exposed to the risk and costs of losing the trust of their customers, employees and partners.
Emerging technologies tied to algorithmic trust include secure access service edge (SASE), differential privacy, authenticated provenance, bring your own identity, responsible AI and explainable AI.
Beyond silicon — For more than four decades, Moore’s Law (the number of transistors in a dense integrated circuit (IC) doubles about every two years) has guided the IT industry. As technology approaches the physical limits of silicon, new advanced materials are creating breakthrough opportunities to make technologies faster and smaller.
Critical technologies to be considered include DNA computing, biodegradable sensors and carbon-based transistors.