Artificial intelligence. Freefall. Dzhimsher Chelidze

work of dozens of weak models.

      But now strong AI does not fit in with ESG in any way, environmentalists like it commercial. Its creation is possible only within the framework of strategic and national projects financed by the state. And here is one of the interesting facts in this direction: the former head of the US National Security Agency (until 2023a), retired General, Paul Nakasone joined the board of directors of Open AI in 2024. The official version is for organizing Chat GPT security.

      I also recommend reading the document titled “Situational Awareness: The Coming Decade”. Its author is Leopold Aschenbrenner, a former Open AI employee from the Super alignment team. The document is available by QR-code and hyperlink.

      SITUATIONAL AWARENESS

      A shortened analysis of this document is also available using the QR-code and hyperlink below.

      Analysis of the AGI document by Leopold Aschenbrenner, former Open AI employee

      To simplify it completely, the author’s key theses are:

      – By 2027, strong AI (AGI) will become a reality.

      I disagree with this statement. My arguments are given above, plus some of the theses below and risk descriptions from the authors say the same. But again, what is meant by the term AGI? I have already given my own definition, but there is no single term.

      – AGI is now a key geopolitical resource. We forget about nuclear weapons; this is the past. Each country will strive to get AGI first, as in its time an atomic bomb.

      The thesis is controversial. Yes, this is a great resource. But it seems to me that its value is overestimated, especially given the complexity of its creation and the mandatory future errors in its work.

      – Creating an AGI will require a single computing cluster worth a trillion US dollars. Microsoft is already building one for Open AI.

      Computing power also requires spending on people and solving fundamental problems.

      – This cluster will consume more electricity than the entire US generation.

      We discussed this thesis above. More than a trillion dollars is also invested in electricity generation, and there are also risks.

      – AGI funding will come from tech giants – already Nvidia, Microsoft, Amazon, and Google are allocating $100 billion a quarter to AI alone.

      I believe that government funding and, consequently, intervention is essential.

      – By 2030, annual investment in AI will reach $8 trillion.

      Excellent observation. Now the question arises, is this economically justified?

      Despite all the optimism of Leopold Aschenbrenner regarding the timing of the creation of AGI, he himself notes a number of limitations:

      – Lack of computing power for conducting experiments.

      – Fundamental limitations associated with algorithmic progress

      – Ideas are becoming more complex, so it is likely that AI researchers (AI agents who will conduct research for people) will only maintain the current rate of progress, and not increase it significantly. However, Aschenbrenner believes that these obstacles can slow down, but not stop, the growth of AI systems ' intelligence.

      Chapter 3. What can weak AI do and general trends?

      Weak AI in applied tasks

      As you, probably already, understood, I am a proponent of using what is available. Perhaps this is my experience in crisis management, and whether it’s just an erroneous opinion. But still, where can the current weak AI based on machine learning be applied?

      The most relevant areas for applying AI with machine learning are:

      – forecasting and preparing recommendations for decisions taken;

      – analysis of complex data without clear relationships, including for forecasting and decision-making;

      – process optimization;

      – image recognition, including images and voice recordings;

      – automating the execution of individual tasks, including through content generation.

      The new direction, which is at its peak of popularity in 2023—2024, is image recognition, including images and voice recordings, and content generation. This is where the bulk of AI developers and most of these services come from.

      At the same time, the combination of AI + IoT (Internet of Things) deserves special attention:

      – AI receives pure big data, which does not contain human errors, for training and finding relationships.

      – The effectiveness of IoT increases, as it becomes possible to create predictive analytics and early detection of deviations.

      Key trends

      – Machine learning is moving towards an increasingly low entry threshold.

      One of the tasks that developers are currently solving, is to simplify the creation of AI models to the level of site designers, where special knowledge and skills are not needed for basic application. The creation of neural networks and data science is already developing according to the “service as a service” model, for example, DSaaS – Data Science as a Service.

      You can start learning about machine learning with AUTO ML, its free version, or DSaaS with initial audit, consulting, and data markup. You can even get data markup for free. All this reduces the entry threshold.

      – Creating neural networks that need less and less data for training.

      A few years ago, to fake your voice, it was necessary to provide a neural network with one or two hours of recording your speech. About two years ago, this indicator dropped to a few minutes. Well, in 2023, Microsoft introduced a neural network that takes just three seconds to fake.

      Plus, there are tools, that you can use to change your voice even in online mode.

      – Create support and decision-making systems, including industry-specific ones.

      Industry-specific neural networks will be created, and the direction of recommendation networks, so-called “digital advisors” or solutions of the class “support and decision-making systems (DSS) for various business tasks” will be increasingly developed.

      Practical example

      We will look at this case again and again, as it is my personal pain and the product I am working on.

      There is a problem in project management – 70% of projects are either problematic or fail:

      – the average excess of planned deadlines is observed in 60% of projects, and the average excess is 80% of the original deadline;

      – 57% of projects exceed their budgets, while the average excess is 60% of the initial budget;

      – failure to meet the success criteria – in 40% of projects.

      At the same time, project management already takes up to 50% of managers ' time, and by 2030 this figure will reach 60%. Although at the beginning of the 20th century, this figure was 5%. The world is becoming more and more volatile, and the number of projects is growing. Even sales are becoming more and more “project-based”, that is, complex and individual.

      And what does such project management statistics lead to?

      – Reputational