Proprietary + Confidential Madrid, November 2024 AI for Renaissance Humans Beings Andrés-Leonardo Martínez-Ortiz a.k.a almo By JPxG - DALL-E 3, Public Domain, https://commons.wikimedia.org/w/index.php?curid=144161107

About me… Andrés-Leonardo Martínez-Ortiz a.k.a almo, holds a PhD on Software, Systems and Computing and a Master on Computer Science. Based on Zurich, almo is a member of the Vertex AI SRE Site Reliability Engineering team, leading several programs aiming for reliability, efficiency & convergence. Main editor of the Cathartic Computing Club newsletter and member of IEEE, ACM, Linux Foundation and Computer Society. @davilagrau almo.dev almo Do you want join the Cathartic Computer Club? Sign up here 👉

Disclaimer

Yes, I did this presentation using AI

Photo by Ben Sweet on Unsplash Actually… I am an AI*! You all are AI! and the universe might be an hologram! * AI stands for Augmented Intelligence

AI Metaphysics

Intelligence Adjusting the parameters of a mental model Minimizing errors Exploring the space of possibilities Restricting the search space Optimizing a reward function Projecting a priori hypotheses Exploiting the Combinatorial Explosion (multi-level, multimodal)

Artificial Incomplete Formal Systems Quantum Mechanics Runaway solution of Dirac Equation

Artificial Intelligence Heuristic computing Symbolic computing Perception computing Machine learning computing Reinforcement Learning computing Generative computing

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AI Psychoanalysis

👉 https://ai.google.dev/gemini-api/prompts But also you can find free prompting libraries @ Hugging Face or @ Anthropic https://docs.anthropic.com/en/prompt-library/library

ML Commons https://mlcommons.org/benchmarks EFF Measuring the Progress of AI Research (2017) https://www.eff.org/ai/metrics Massive Multitask Language Understanding https://github.com/hendrycks/test General Language Understanding Evaluation (GLUE) https://gluebenchmark.com AI Index Annual Report 2024 https://aiindex.stanford.edu/report

👉 ARC-AGI measures progress towards artificial general intelligence using a private evaluation dataset https://arcprize.org

AI Dialectic By Jakob Schlesinger - anagoria, Public Domain, https://commons.wikimedia.org/w/index.php?curid=33762804

Increasing prioritization and budget ˜80% of the enterprises have AI as priority ˜83% of the enterprises increase the AI budget 49% process automation 45% managing logistics 43% supply chain optimization 57% customer experience 50% customer insights 48% customer interaction Increasing number of use cases Increasing saving and adoption speed thanks to specialized partnership IT partners allow savings up to 30% of the time IT partners demand ˜25% less of internal IT resources IT partners allow savings up to 20% of the budget Scale decreasing efficiency ˜40% of the enterprises expend +50% in development Increasing deployment time (+64% MoM) Maturity long curve 55% of the enterprises on evaluation or early stage of development and deployment Multiple & fragmentated success metrics make difficult the evaluation Software supply chain and configuration management diminish AI quality 36% of the enterprises have serious problem with model performance 56% of the enterprises have serious problem the security and auditing models 67% of the enterprises have to be complaint to several quality standards.

Thesis Transformative potential of AI to revolutionize industries, solve complex problems, and improve human lives. Ability to automate tasks, analyze data, and generate creative solutions, leading to increased efficiency, innovation, and economic growth. Disruptive Technology Exponential Technology

Antithesis Potential dangers, such as job displacement, algorithmic bias, privacy violations, and the potential for autonomous weapons systems to escalate conflicts. Peed for careful regulation, ethical guidelines, and safeguards to prevent unintended consequences and ensure AI benefits all of humanity

Improvement Areas Sensitivity Current algorithms exhibit high sensitivity to variations in input data. Adversarial attacks are capable of disrupting AI solutions by introducing noise that is often imperceptible. Bias Current algorithms often exhibit significant biases, stemming from the cultural context of the development teams or the training data. These are intrinsic biases that are not always easy to identify and mitigate. This makes their application difficult in scenarios with moral implications. Retention Current algorithms respond to training data without the ability to "store" history. This effect is catastrophic in time series data that extends over long periods. Common Sense Current algorithms are incapable of using common sense. Reaching a decision based on globally available information and common knowledge accessible to people is still a challenge yet to be solved. Justifiable Current algorithms do not allow for adequate justification of the reasoning they follow to reach their solutions. In this sense, they behave like black boxes, making their application difficult in scenarios with moral implications. Risk Analysis Currently, models provide average levels of accuracy. For scenarios with moral implications, risk or accuracy assessment mechanisms are necessary to allow for human intervention. This functionality is not available in most cases.

Temporal Model Drifting

How does the temporal ML drifting look like? Exotic patterns: chaos and periodic Vela, D., Sharp, A., Zhang, R. et al. Temporal quality degradation in AI models. Sci Rep 12, 11654 (2022).

Data (including contrafactual data)

Synthesis Are we there yet?

#1 AI beats humans on some tasks, but not on all. #2 Industry continues to dominate frontier AI research #3 Frontier models get way more expensive #4 The United States leads China, the EU, and the U.K. as the leading source of top AI models. #5 Robust and standardized evaluations for LLM responsibility are seriously lacking. #6 Generative AI investment skyrockets. #7 The data is in: AI makes workers more productive and leads to higher quality work. #8 Scientific progress accelerates even further, thanks to AI. #9 The number of AI regulations in the United States sharply increases. #10 People across the globe are more cognizant of AI’s potential impact—and more nervous. AI Index Annual Report 2024 https://aiindex.stanford.edu/report

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AI Index Annual Report 2024 https://aiindex.stanford.edu/report

And thus, we are not there yet… maybe we do not even know where we're going yet…

Future Design & Use Cases https://worldbuild.ai

δυστόπος

δυστόπος

Resources DeepMind, Technologies https://deepmind.google/technologies Hugging Face https://huggingface.co Institute for Human-Centered AI https://hai.stanford.edu https://aiindex.stanford.edu Artificial Intelligence, Our World In Data https://ourworldindata.org/artificial-intelligence arXiv Artificial Intelligence https://arxiv.org/list/cs.AI/recent The Batch https://www.deeplearning.ai/the-batch Do you want join the Cathartic Computer Club? Sign up here 👉

Thank you!