analycat

Too Much Data: The Challenge of Rare Events

In this clip, Adi Hazan from Analycat discusses the nature of data, particularly in IoT (Internet of Things) contexts. He notes that most data is repetitive, with significant events being rare yet crucial. For example, a device on fire won’t send data, making the absence of data extremely important, yet often overlooked. He compares this to language, where rare words convey specific meanings. He suggests that the challenge lies in identifying these rare, meaningful events amidst vast amounts of data,… Read more

The Algorithm and Shrinking Choices

In this clip, Adi Hazan from Analycat highlights how algorithms, like those on Netflix and online platforms, narrow our choices by promoting certain options. He illustrates this with his own rare MP3s, not found on streaming services, emphasizing the growing challenge in discovering unique content. Find out more about Analycat -> Here. Analycat

Beyond Data Absorption: – Asking Data Questions

In this clip, Adi Hazan from Analycat explains a problem they have addressed in data analysis: the importance of not just absorbing data, but also asking questions. He describes their ability to analyze all possibilities and recognize gaps in knowledge. Using an insurance company as an example, he illustrates how underwriters specialize in certain types of risks, such as power stations, based on their profitable history. These underwriters’ expertise is not reflected in the data, especially regarding excluded individuals. He… Read more

Enormous Costs: The Energy Impact of AI

In this clip, Adi Hazan from Analycat discusses the significant costs associated with modern image-generating AI programs. He points out that while these programs can produce impressive results, like generating images of “banana fish,” they require thousands of computing operations. These operations consume substantial amounts of electricity, to the extent that data centers worldwide are now using more electricity than the entire United Kingdom. He highlights the growing awareness among companies and individuals about the environmental impact and energy consumption… Read more

Size the Solution to the Problem

In this clip, Adi Hazan from Analycat uses the analogy of moving sand to discuss the appropriate use of technology in problem-solving. He states that while a backhoe loader is generally considered the best tool for moving sand, a shovel and bucket might be more suitable for a smaller amount of sand. Similarly, in the context of AI and big data, he suggests that out of 100 cases that might initially seem to require AI, 95 of them probably don’t… Read more

Human Verification Still Needed

In this clip, Adi Hazan from Analycat discusses the appeal and limitations of AI models. He finds it enticing to have larger, better, or personalized models. However, he cautions that these models cannot discern truth from fiction, a phenomenon he refers to as “hallucinating.” He emphasizes the necessity of human verification of AI outputs, noting that currently, the most effective use of AI is in conjunction with human cross-checking. He also observes a trend away from trying to replace humans… Read more