Check in on AI – What's Changing

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It's been almost two years since I sounded the alarm and declared – without a shadow of a doubt – that AI would be the biggest transformation in human history. Considering things like stone tools, fire control, wheels, clothing, agriculture, the alphabet, printing, electricity, flight, TV, computers, space and the Internet, that's a big statement – ​​and I mean it. Repeatedly invoked, mostly by conservative thinkers and Luddites. Please note, the use of the word 'conservative' has no political connotation. It simply describes people who are reluctant to recognize anything that doesn't fit their paradigms.

I went on to say that the nature, pace and scope of change brought about by AI will be difficult to grasp at first, but we will get there very soon.

I think, for the most part, we're there, at least in theory. I certainly hope so, because as an independent career coach and consultant to corporate leaders, I better be right.

Here we are now, two years later, and we no longer need to talk about AI in theoretical terms. AI has a history — and what's happened in AI in the last couple of years, I think Bertrand Russell suggests — is “in all cases it's a healthy thing and then to question things that you've long taken for granted.” I have understood for a long time.” – Baba is.

How has AI changed?

So I decided it was time to once again call upon my esteemed brain trusts—six thinkers with big minds and big imaginations—and I asked them one question: How has AI changed in two years? ? This should show us how fast things are moving.

AI Ethics and Bias Mitigation

As AI took off, it was one of the first major areas of concern. It just got bigger. Algorithm development has become a front-and-center issue, particularly for ethics and data bias. Researchers and practitioners are working closely together here.

AI for Social Good

In our world of stubbornly unsolved social problems—poverty, lack of access to clean water, health care disparities, illiteracy, environmental justice, humanitarian crises—we now have a means to solve them all. Not unless we boost AI.

Continuous development in deep learning

Deep learning techniques have grown in areas such as computer vision, natural language processing, and reinforcement learning. Modeling is larger and more capable, enabling higher performance for those who master it.

Dominance of Transformer Architecture

Transformer-based models, with architectures such as BERT and GPT, have become more dominant in NLP tasks.

Advances in reinforcement learning

Researchers are making great progress in areas such as sample efficiency, transfer learning, and robustness. RL algorithms are being applied to real-world problems such as robotics.

Advances in AI hardware

As AI grows exponentially, the hardware it runs on=re, it will have to too. Advances such as GPUs, TPUs, and neuromorphic chips are in the works to support the increasing computational demands of AI algorithms. It includes energy efficient hardware.

AI Governance

By nature, careful regulation and warp speed technological advances are at war with each other. Governments and regulatory agencies are establishing guidelines for the responsible use of AI in areas such as privacy, accountability, and social impact.

AI and Cybersecurity

AI is increasingly important in cybersecurity, threat detection, and fighting malware. Defense against AI attacks is also an active area of ​​research.

one more thing…

My guru urged me to tell you that between posting this report and the time you read it … it will already need an update.

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