To feel the pulse of the fast changing AI landscape is like taking the vital signs of a sprinter in action. Based on the most recent news as of September, 2024, here is a macro picture of some GenAI and AI application:
“Hectocorn”: A new word “hectocorn” has been invented to describe AI behemoths such as OpenAI and Anthropics, backed by multi billion dollar investments from tech giants such as Microsoft and Amazon. (This is relative to the word “unicorn” and “smaller” sized VC investments in millions or hundreds of millions.)
“Scaling laws”: Neither does the word “blitzscaling” measure up to the scale of GenAI. The newly coined “scaling laws” for GenAI means: “The more computing power and data that you throw at AI, the cleverer models become. You thus have to invest fistfuls of money upfront to develop a competitive product, or else invent a new approach”, says “OpenAI’s new fundraising is shaking up Silicon Valley,” The Economist’s Magazine, September 19, 2024, emphasis added. https://www.economist.com/business/2024/09/19/openais-new-fundraising-is-shaking-up-silicon-valley)
While tech giants and sovereign funds are backing the “hectocorns” with investments of stratospheric sizes, “scientific breakthroughs in model-building could upend the industry”, “especially given the competition it faces from smaller, cheaper models, some of which are at least partially open-source”, supra.
AI investors and companies are developing specific AI processors that are faster and more resource-efficient than “general-purpose chips.”
“[C]ompanies are developing chips especially for the operations needed to run large language models. This specialization means that they can run more efficiently than more general-purpose processors, such as Nvidia’s. Alphabet, Amazon, Apple, Meta and Microsoft are all designing their own AI chips. More money has flowed into funding AI-chip startups in the first half of this year than in the past three combined,” quoted from “The breakthrough AI needs”, The Economist’s Magazine, September 19, 2024, emphasis added. https://www.economist.com/leaders/2024/09/19/the-breakthrough-ai-needs
“Developers are also making changes to AI software. Bigger models that rely on the brute force of computational power are giving way to smaller and more specialized systems. OpenAI’s newest model, o1, is designed to be better at reasoning, but not generating text. Other makers are employing less onerous calculations, so as to make more efficient use of chips. Through clever approaches, such as using a mixture of models, each suited to a different type of problem, researchers have drastically cut down on processing time. All this will change how the industry operates.” “If the trend towards smaller and more specialized models continues, then the AI universe could contain a constellation of models, instead of just a few superstars.” Supra.
“Artificial intelligence (AI) applications are software programs that use AI techniques to perform specific tasks. These tasks can range from simple, repetitive tasks to complex, cognitive tasks that require human-like intelligence.” https://cloud.google.com/discover/ai-applications
There are countless ways AI can be applied to our daily work and living. As of now, the most common AI applications among industries include natural language processing (NLP), used in communication and sentiment analysis, translation, spam filtering, etc., Machine learning (ML) that helps with fraud detection, system improvement recommendations, predictive analytics, and other data-intensive work; Robotics used in manufacturing, healthcare, labor-intensive construction,services, and operations; Computer vision used in facial recognition, object detection, and self-driving vehicles to identify and interpret visuals. Supra.
Many of the emerging smaller, less expensive, specialized AI models require bespoke design and development of AI applications.
In software, healthcare, manufacturing, and BI industries, where we at Wull provide management and AI consulting services, as enterprise LLMs are built from proof to scale, and AI use cases enable higher levels of security, efficiency, and complexity, it is foreseeable that AI applications will result in speedy changes to operations and systems. Entire organizations and industries will need to be prepared for disruptive changes before the tidal wave hits.
However, AI per se is not a be-all and end-all, but a means to the end of achieving goals, mission, and vision of an organization. The bedrock and the principles of business management, growth, and operations are not a fashion or a fad. Leaders need to vigilantly adhere to foundational principles while developing AI tools or other technology. Companies need to first develop and examine their data strategies, and formulate a comprehensive AI approach.
©Stephen Wullschleger all rights reserved.
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