Artificial intelligence. Freefall. Dzhimsher Chelidze

growth.

      The most common and critical errors are:

      – unclear formulation of project goals, results, and boundaries;

      – insufficiently developed project implementation strategy and plan;

      – inadequate organizational structure of project management;

      – an imbalance in the interests of project participants;

      – ineffective communication within the project and with external organizations.

      How do people solve this problem? Either they don’t do anything and suffer, or they go to school and use task trackers.

      However, both approaches have their pros and cons. For example, classical training provides an opportunity to ask questions and practice various situations during live communication with the teacher. At the same time, it is expensive and usually does not imply further support after the end of the course. Task trackers, on the other hand, are always at hand, but they do not adapt to a specific project and company culture, do not contribute to the development of competencies, but on the contrary, are designed to monitor work.

      As a result, after analyzing my experience, I came up with the idea of a digital advisor – artificial intelligence and predictive recommendations “what to do, when and how” in 10 minutes for any project and organization. Project management becomes available to any manager conditionally for a couple of thousand rubles a month.

      The AI model includes a project management methodology and sets of ready-made recommendations. The AI will prepare sets of recommendations and gradually learn itself, finding new patterns, and not be tied to the opinion of the creator and the one who will train the model at the first stages.

      Chapter 4. Generative AI

      What is generative artificial intelligence?

      Earlier, we reviewed the key areas for applying AI:

      – forecasting and decisions-making;

      – analysis of complex data without clear relationships, including for forecasting purposes.

      – process optimization;

      – image recognition, including images and voice recordings.

      – content generation.

      The areas of AI that are currently at the peak of popularity, are image recognition (audio, video, numbers) and content generation based on them: audio, text, code, video, images, and so on. Generative AI also includes digital Expert Advisors.

      Generative AI Challenges

      As of mid-2014, the direction of generative AI cannot be called successful. For example, in 2022, Open AI suffered a loss of $540 million due to the development of ChatGPT. And for further development and creation of a strong AI, about $ 100 billion more will be needed. This amount was announced by the head of Open AI himself. The same unfavorable forecast for 2024 is also given by the American company CCS Insight.

      For reference: the operating cost of Open AI is $ 700,000 per day to maintain the chat bot ChatGPT.

      The general trend is supported by Alexey Vodyasov, Technical Director of SEQ: “AI does not achieve the same marketing results that we talked about earlier. Their use is limited by the training model, and the cost and volume of data for training is growing. In general, the hype and boom are inevitably followed by a decline in interest. AI will come out of the limelight as quickly as it entered, and this is just the normal course of the process. Perhaps not everyone will survive the downturn, but AI is really a” toy for the rich”, and it will remain so in the near future.” And we agree with Alexey, after the hype at the beginning of 2023, there was a lull by the fall.

      Adding to the picture is an investigation by the Wall Street Journal, according to which the majority of IT giants have not yet learned how to make money on the capabilities of generative AI. Microsoft, Google, Adobe and other companies that invest in artificial intelligence, are looking for ways to make money on their products. Here are some examples:

      – Google plans to increase the subscription price for AI-enabled software;

      – Adobe sets limits on the number of requests to services with AI during the month.

      – Microsoft wants to charge business customers an additional $30 per month for the ability to create presentations using a neural network.

      Well, and the icing on the cake-calculations by David Cahn, an analyst at Sequoia Capital, showing that AI companies will have to earn about $600 billion a year to offset the costs of their AI infrastructure, including data centers. The only, one who now makes good money on AI, is the developer of Nvidia accelerators.

      More information about the article can be found in the QR-code and hyperlink below.

      AI industry needs to earn $600 billion per year to pay for massive hardware spend – fears of an AI bubble intensify in wake of Sequoia report

      Computing power is one of the main expenses when working with Gen AI: the larger the server requests, the larger the infrastructure and electricity bills. Only suppliers of hardware and electricity benefit. So, Nvidia in August, 2023 earned about $5 billion thanks to sales of the accelerators for AI A100 and H100 only to the Chinese IT sector.

      This can be seen in two examples on practice.

      First is Zoom tries to reduce costs by using a simpler chatbot developed in-house and requiring less computing power compared to the latest version of ChatGPT.

      Second is the most well-known AI developers (Microsoft, Google, Apple, Mistral, Anthropic, and Cohere) began to focus on creating compact AI models, as they are cheaper and more cost-effective.

      Larger models, such, as Open AI’s GPT-4, which has more than 1 trillion parameters and is estimated to cost more than $ 100 million to build, do not have a radical advantage over simpler solutions in applications. Compact models are trained on narrower data sets and can cost less than $ 10 million, while using less than 10 billion parameters, but solve targeted problems.

      For example, Microsoft introduced a family of small models called Phi. According to CEO Satya Nadella, the model’s solutions are 100 times smaller than the free version ChatGPT, of ChatGPT, but they handle many tasks almost as efficiently. Yusuf Mehdi, Microsoft’s chief commercial officer, said the company quickly realized that operating large AI models is more expensive than initially thought. So, Microsoft started looking for more cost-effective solutions.

      Apple also plans to use such models to run AI directly on smartphones, which should increase the speed and security. At the same time, resource consumption on smartphones will be minimal.

      Experts themselves believe that for many tasks, for example, summarizing documents or creating images, large models may generally be redundant. Ilya Polosukhin, one of the authors of Google’s seminal 2017 article on artificial intelligence, figuratively compared using large models for simple tasks to going to the grocery store on a tank. “Quadrillions of operations should not be required to calculate 2 +2,” he stressed.

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