AI Macro Trends for 2024

With the continuing Presidential Resurrection of Donald Trump, the self-immolation of Hamas and Israel States, and Taylor Swift’s latest Fling with Love, Artificial Intelligence has garnered a new Social Meme-ing. So  half-a-dozen AI-Artificial Intelligence macrotrends are easily surmised, leading to consensus conclusions in 2024. Business Analysts see AI tools taking off just like internet technology 30 years before.

Starting in the 1950s to the 1990s internet development became a key system driver. Moore’s Law’s automatic hardware advances propelled new software linkages and interconnections to new level of innovation. For example, in the 1950’s and 60’s,  TCP and IP protocols allowed supercomputers to be time-shared and interconnected in large campus networks. In the 1970s, work on ARPANET utilizing cheap minicomputers and datablock offerings soon produced widespread 1000 node  networks. So the introduction of browsers and HTML/JavaScript/CSS technology mixed with ever-cheaper transmission devices helped launch Wikis and all the WWW-World Wide Web innovations.

AI-Artificial Intelligence has seen a similar blossoming of interrelated technologies that are adding up to a fast-growing AI Wave:

Statista shows a $250 billion market in 2024 rising to $600 billion in 2026.

Precedence Research sees even larger AI market growth, from $638 billion in 2024 to $900 billion in 2026. Both consultants agree that Generative AI used in healthcare, business customer services, chat support, financial and insurance services will drive the market for the next 2-3 years. Also, both consultants see USA. European Union, and Asia Pacific experiencing the fastest AI growth. But a lot depends on the development and delivery of AI technology.

Here is the sequence of electronic and software technology advances that are propelling AI currently:
1 –  Moore’s Law continues to deliver the same computing power for half the cost every two years. This trend is vital across the board for AI systems, as PCs, routers, AI servers, database engines, and transmission devices also show similar performance, cost, and reliability improvements.
2 – AI traditional or rule-based tools were first to demo AI use in computationally complex tasks- An AI Gen program learned to beat the best chess, shogi, and go players in a unique fashion. Rule-based AI has been used in prospecting systems or financial planning, where large steps/stages have to be foreseen and calculated out.
3 –

The Systems consulting community sees a major role for AI in Business Systems over the next 5 years as AI tools improve operations while satisfying customer needs. Lets see the many ways in which AI is expected to be creatively deployed.


Gartner envisages 5 major AI adoption trends
1 – Data ecosystems are moving from self-contained software or blended deployments to full cloud solutions for broader reach and better response time.
2 – Edge AI will enable the processing of data at the point of creation on the edge, helping organizations to gain real-time insights, detect new patterns and meet stringent data privacy requirements This is part of the adoption of multi-modal AI.
3 – Responsible AI transforms AI into a positive force, rather than a threat to society and to itself. It outlines many aspects of making the right business and ethical choices  such as business and societal value, risk, trust, transparency and accountability. It still has tenuous community commitment.
4 – Data-centric AI means a shift from being model- and code-centric to being more data focused to build better AI systems.  AI-specific data management embraces synthetic data and data labeling technologies, aiming to solve many data challenges, including accessibility, volume, privacy, security, complexity and scope.
5 – Senior management AI commitment is reflected in a recent Gartner poll of more than 2,500 executive leaders found that 45% reported that ChatGPT buzz prompted them to increase AI investments. 75% said their organization is investigating generative AI, while 19% are in pilot or production mode.

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TechTarget depicts the latest AI opportunity trends.
1 -AI multi-modal tools do 2 things. First, text prompts, verbal statements, image/video clips and sound signals  are combined to help shape AI responses and deliver better outputs. Second, AI tools are nearing the ability to correlate and then respond to different input signals. This area is on the AI frontlines as seen below.
2 – Agentic AI marks a significant shift from rule-based reactive AI to a proactive multimodal approach. AI agents are imagined as  many-input control signals that  measure risk-state, goal opportunity, and changing relevant conditions. For an Agentic example, in environmental monitoring, an AI agent would collect data, analyze patterns and initiate preventive actions in response to varying hazards.
3 – Open Source AI looks to marshal the efforts of many players via some freely accessible AI software advances in exchange for not much as the AI majors[ChatGPT, Meta, Google, Microsoft, etc.] control the most powerful LLM models and dole out access to AI test environs. See recent Google 1, 2  , Meta 1, ChatGPT 4, IBM 3, for the state of AI accessibility from major AI vendors.
4 – RAG-Retrieval Augmented Generation uses a second input mode to diminish AI Hallucination errors where LLM models derive false conclusions. In AI Business models this is unacceptable results. RAG significantly improves results while reducing the size and cost of LLM models.
5. Customized enterprise generative AI models – uses RAG and shortcut methods to adapt major LLM models to niche-specific apps. The problem is that less-than-safe model methods increases  chance of AI Hallucination errors or breaking fair-use rules.
6 – Scarcity of AI and machoine learning talent  is 

 

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