Artificial Intelligence can enhance human intelligence.AI systems if used can be very beneficial in cybersecurity, AI systems can be trained to generate alerts for threats, identify new types of malware and protect sensitive data and could be very useful against cyber attacks. According to research, a midsize company get alerts for over 200,000 cyber events every day. Some of these threats will therefore, naturally go unnoticed and cause severe damage to company and data. A lot of activity happens on a company's network. An mid-sized company itself has huge traffic.

Some of the worst sectarian clashes since Lebanon's 15-year civil war (1975-1990) broke out in Beirut this week between supporters of Hezbollah and Amal, both Shiite political parties, and Christian, far-right Lebanese Forces. Shiite protesters were rallying against the state probe into the Beirut port blast, which occurred last year. They say authorities were singling out Shiite politicians for questioning and blame. In this video, watch Ian Bremmer's conversation with Lebanese journalist and author Kim Ghattas on GZW talking about the future of Lebanese politics and sectarianism in the county after the after the blast. It was originally published on August 19, 2020.

Economic revitalization minister Daishiro Yamagiwa on Sunday revealed a plan to set up a fund to support the development of cutting-edge technologies crucial for the country's economic security. "The government will fully support private-sector companies' research and development activities for advanced technologies, and their efforts to prepare a business environment for such technologies," he said during a television program. The fund will likely be worth about ¥100 billion. The government will include the planned fund in a package of economic measures to be drawn up after the Oct. 31 Lower House election. The fund is expected to help Japanese companies and universities develop artificial intelligence, quantum and robot technologies, biotechnology and other important tech, and put them into practical use.

Just when you thought it couldn't grow any more explosively, the data/AI landscape just did: the rapid pace of company creation, exciting new product and project launches, a deluge of VC financings, unicorn creation, IPOs, etc. It has also been a year of multiple threads and stories intertwining. One story has been the maturation of the ecosystem, with market leaders reaching large scale and ramping up their ambitions for global market domination, in particular through increasingly broad product offerings. Some of those companies, such as Snowflake, have been thriving in public markets (see our MAD Public Company Index), and a number of others (Databricks, Dataiku, DataRobot, etc.) have raised very large (or in the case of Databricks, gigantic) rounds at multi-billion valuations and are knocking on the IPO door (see our Emerging MAD company Index). But at the other end of the spectrum, this year has also seen the rapid emergence of a whole new generation of data and ML startups. Whether they were founded a few years or a few months ago, many experienced a growth spurt in the past year or so. Part of it is due to a rabid VC funding environment and part of it, more fundamentally, is due to inflection points in the market. In the past year, there's been less headline-grabbing discussion of futuristic applications of AI (self-driving vehicles, etc.), and a bit less AI hype as a result. Regardless, data and ML/AI-driven application companies have continued to thrive, particularly those focused on enterprise use trend cases. Meanwhile, a lot of the action has been happening behind the scenes on the data and ML infrastructure side, with entirely new categories (data observability, reverse ETL, metrics stores, etc.) appearing or drastically accelerating. To keep track of this evolution, this is our eighth annual landscape and "state of the union" of the data and AI ecosystem -- coauthored this year with my FirstMark colleague John Wu. (For anyone interested, here are the prior versions: 2012, 2014, 2016, 2017, 2018, 2019: Part I and Part II, and 2020.) For those who have remarked over the years how insanely busy the chart is, you'll love our new acronym: Machine learning, Artificial intelligence, and Data (MAD) -- this is now officially the MAD landscape! We've learned over the years that those posts are read by a broad group of people, so we have tried to provide a little bit for everyone -- a macro view that will hopefully be interesting and approachable to most, and then a slightly more granular overview of trends in data infrastructure and ML/AI for people with a deeper familiarity with the industry. Let's start with a high-level view of the market. As the number of companies in the space keeps increasing every year, the inevitable questions are: Why is this happening? How long can it keep going?

This dissertation studies a fundamental open challenge in deep learning theory: why do deep networks generalize well even while being overparameterized, unregularized and fitting the training data to zero error? In the first part of the thesis, we will empirically study how training deep networks via stochastic gradient descent implicitly controls the networks' capacity. Subsequently, to show how this leads to better generalization, we will derive {\em data-dependent} {\em uniform-convergence-based} generalization bounds with improved dependencies on the parameter count. Uniform convergence has in fact been the most widely used tool in deep learning literature, thanks to its simplicity and generality. Given its popularity, in this thesis, we will also take a step back to identify the fundamental limits of uniform convergence as a tool to explain generalization. In particular, we will show that in some example overparameterized settings, {\em any} uniform convergence bound will provide only a vacuous generalization bound. With this realization in mind, in the last part of the thesis, we will change course and introduce an {\em empirical} technique to estimate generalization using unlabeled data. Our technique does not rely on any notion of uniform-convergece-based complexity and is remarkably precise. We will theoretically show why our technique enjoys such precision. We will conclude by discussing how future work could explore novel ways to incorporate distributional assumptions in generalization bounds (such as in the form of unlabeled data) and explore other tools to derive bounds, perhaps by modifying uniform convergence or by developing completely new tools altogether.

At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I was working on exotic niche problems. Well, the field has matured substantially and here I propose to have a look at the top applications of GNNs that we have recently had. If this in-depth educational content on graph neural networks is useful for you, you can subscribe to our AI research mailing list to be alerted when we release new material.

Nicolas Chaillan, the Pentagon's former Chief Software Officer, is on a whirlwind press tour to drum up as much fervor for his radical assertion that the US has already lost the AI race against China. We have no competing fighting chance against China in 15 to 20 years. Right now, it's already a done deal. Chaillan's departure from the Pentagon was preceded by a "blistering letter" where he signaled he was quitting out of frustration over the government's inability to properly implement cybersecurity and artificial intelligence technologies. Tickets to TNW Conference 2022 are available now! And, now, he's telling anyone who will listen that the US has already lost a war to China that hasn't even happened yet.

The European Union wants its Artificial Intelligence (AI) Act to be an example for the rest of the world to follow when regulating the emerging technology. EU Telecommunications ministers held their first debate on the proposed AI Act in Brussels on Thursday to decide the guidelines for the coming years, where Slovenia's Minister for Public Administration Boštjan Koritnik said the bloc's AI act should serve as a global model. "Ministers today voiced their clear support for one comprehensive law on artificial intelligence, which would serve as a model across the globe, in the same vein as the general data protection regulation, GDPR, in the area of protection of personal data," Koritnik said. "There is still substantial work ahead, as we want to make sure that the Artificial Intelligence Act will achieve its twin aims of ensuring safety and respect for fundamental rights and stimulating the development and uptake of AI-based technology in all sectors. The Slovenian [European Council] presidency will continue the intense work on this proposal, which it considers a top priority in the digital area."

The United States has offered unspecified condolence payments to the families of the 10 civilians, including seven children, who were mistakenly killed in the Aug. 29 drone strike in Kabul that took place shortly before American troops withdrew from Afghanistan. The Pentagon also said it's working with the State Department to support family members who may want to relocate to the United States. The U.S. military insisted for almost three weeks that the drone strike was justified, claiming it had stopped an attack planned for Kabul's airport. But it later changed its tune amid an overwhelming amount of evidence. Weeks after the Pentagon acknowledged the strike had hit civilians, Colin H. Kahl, the under secretary of defense for policy, held a virtual meeting with Steven Kwon, the founder and president of Nutrition & Education International.

AI surveillance is increasing at a rapid pace around the world. The East Asia/Pacific, Americas, and the Middle East/North Africa regions are robust adopters of these tools. Even liberal democracies in Europe have installed automated border controls, predictive policing, "safe cities", and facial recognition systems. China is the biggest supplier of these technologies which can be found in 63 countries. Huawei alone is responsible for providing AI surveillance technology to at least fifty countries and its leadership has strong ties with the Chinese government.