At what was billed as a "fireside chat" at Tel Aviv University in June 2023, the very first question from the audience posed to OpenAI CEO Sam Altman and chief scientist Ilya Sutskever was, "Could open source LLMs (large language models) potentially match GPT-4's abilities without additional technical advances, or is there a'secret sauce' in GPT-4 unknown to the world that sets it apart from the other models?" After nervous laughter and applause, Sutskever said, "You don't want to think about it in binary black-and-white terms where there is a secret sauce that will never be rediscovered," adding that perhaps someday, an open source model would reproduce GPT-4--"but when it will be, there will be a much more powerful model in the companies, so there will always be a gap between the open source models and the private models, and this gap may even be increasing." In the ensuing months, despite Sutskever's caution that binary thinking about future AI development methods is too simplistic, there have been numerous opinions published that proclaim diametrically opposed opinions about whether or not open sourcing AI, particularly generative AI, is an imperative social necessity to counter corporate concentration, or opening an existentially threatening Pandora's box of anarchic instructions on how to make weapons or promulgate disinformation on massive scales. Examples of these seemingly incompatible opinions include "Make No Mistake – AI Is Owned by Big Tech," published in MIT Technology Review, and "Open-Source AI Is Uniquely Dangerous," published in IEEE Spectrum. The question regarding complex and nuanced reality around open source AI, especially in the context of large language models, however, is not whether or not it will emerge as a powerful force.

It's no secret that AI can give organizations a major competitive advantage, whether it be predicting supply and demand surges or providing personalized recommendations. One way we're seeing an increase in AI being used is for its ability to anticipate and mitigate risks across organizations. Companies around the globe are leveraging AI to ensure legal compliance and mitigate risk. In fact, Gartner predicted that privacy-driven spending on compliance tooling would rise to $8 billion worldwide throughout 2022. The interest in adopting AI for risk management efforts is fueled by increasing data regulations and traditional methods of data oversight becoming unreliable, given the large volumes of data that organizations are handling.

This search for the pieces of the formula is what I had to do when I started working a few months ago on the product side of TeachableHub's machine learning deployment platform. Before starting to search for any secret formula for successful ML model operationalization, we need to take a really good look at the problem and the tools we are planning to use. Fear of missing out makes them grab on to machine learning and start looking for a problem it can solve, instead of looking for it as a solution to an already established issue. "Companies that are starting with the problem first and improving on a defined metric are the ones that will treat their ML models as a continuously developing product". In other words, the operationalization of machine learning is a process of making continuous progress towards better addressing and solving an established problem.

Artificial Intelligence has the potential to improve governance in terms of accountability, citizen engagement and interoperability. The Indian government has been cottoning on to the benefits of AI since the turn of the last decade. In 2020, the Centre increased the outlay for Digital India to $477 million to boost AI, IoT, big data, cybersecurity, machine learning and robotics. In the 2019 Union Budget speech, Finance Minister Nirmala Sitharaman said the government would offer industry-relevant skill training for 10 million youth in India in AI, Big Data and robotics. However, there is still a lot of scope for not only the Indian government.

In a recent competition for predicting consumer credit risk, AutoAI beat 90% of the participating data scientists. AutoAI is a new tool that utilizes sophisticated training features to automate many of the complicated and time-consuming tasks of feature engineering and building machine learning models, without the need to be a pro at data science. The next video shows a preview of the AutoAI tool. Today's UI is a bit different than the one in the video, which will be generally available soon. You can try it today here.

Every great idea starts with a question. What if we were to…? Sometimes, the problem (especially if you're not a formally trained data scientist) is knowing what question to ask. And more importantly how to ask it. That's where design thinking comes in.

Packets are the lifeblood of the network and most daily computer operations. In virtually every transaction and operation, we leave a network footprint. Only in the case of a local system login accessing applications and data that are locally resident on that system do we not leave a network trace. If we use the Internet, log in via a domain or realm, access a SaaS or other cloud services, or use network-attached storage, we leave a network trace. Because the network is the tie that binds business, leveraging network-focused security analytics is a critical part of security monitoring and response for all connected businesses.

Written by Dr. Kirk Borne I recently learned about the rule of least power in computer programming. The principle expresses the notion that one should use the least powerful programming language to code a given task, while still meeting the business requirements. It occurred to me that a similar concept can apply to data analytics tasks, specifically in data science modeling using machine learning algorithms. In this post, this "doctor of analytics" prescribes this approach for your next analytics project. We have often defined analytics as the products of data science activities that apply machine learning and statistical algorithms on data in order to achieve one or more business requirements.

Because of the government shut down earlier in the year, there was a delay in with IPOs as the SEC could not evaluate the filings. But now it looks like the market is getting ready for a flood of deals. One of the first will be PagerDuty, which was actually founded during the financial crisis of 2009. The core mission of the company is "to connect teams to real-time opportunity and elevate work to the outcomes that matter." Interestingly enough, PagerDuty refers to itself as the central nervous system of a digital enterprise.

Because of the government shut down earlier in the year, there was a delay in with IPOs as the SEC could not evaluate the filings. But now it looks like the market is getting ready for a flood of deals. One of the first will be PagerDuty, which was actually founded during the financial crisis of 2009. The core mission of the company is "to connect teams to real-time opportunity and elevate work to the outcomes that matter." Interestingly enough, PagerDuty refers to itself as the central nervous system of a digital enterprise.