Since their inception, search engines have gone from being basic search agents to sophisticated algorithms based on the artificial intelligence (AI) and machine learning (ML). These innovative technologies affect the Search Engine Optimization (SEO) space from two completely opposite perspectives. On the one hand, it has become much more challenging to promote websites and push them to the top of SERP due to new AI-based ranking algorithms able to perform a very in-depth scan beyond meta. On the other hand, as the overall quality of search results has improved significantly, it is more difficult now to manipulate them using different kludges and black hat practices (albeit still possible which I'll show you below) . All in all, artificial intelligence has fundamentally changed the approach to SEO.

My eye was caught by the title of a working paper published by the National Bureau for Economic Research (NBER): How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI. So I clicked and downloaded, as one does. And then started to read. The paper is an analysis of the 10-K and 10-Q filings that American public companies are obliged to file with the Securities and Exchange Commission (SEC). The 10-K is a version of a company's annual report, but without the glossy photos and PR hype: a corporate nerd's delight.

An executive order was just issued from the White House regarding "the Use of Trustworthy Artificial Intelligence in Government." Leaving aside the meritless presumption of the government's own trustworthiness and that it is the software that has trust issues, the order is almost entirely hot air. The EO is like others in that it is limited to what a president can peremptorily force federal agencies to do -- and that really isn't very much, practically speaking. This one "directs Federal agencies to be guided" by nine principles, which gives away the level of impact right there. Please, agencies -- be guided!

As the volume of cyberattacks grows, security analysts are always on their heels to provide a shield. To address this issue, developers are showing interest in using Machine Learning (ML) to automate threat-hunting. As a sub-field of machine learning, unsupervised learning is making a footprint in detecting malicious content. Resisting cybersecurity challenges with machine learning is not a new thing. Researchers have been working on it since the late 1980s.

Artificial intelligence allows computerized systems to perform tasks traditionally requiring human intelligence: analytics, decision support, visual perception and foreign language translation. AI and robotics process automation, or RPA, have the potential to spur economic growth, enhance national security, and improve the quality of life. In a world of "Big Data" and "Thick Data," AI tools can process huge amounts of data in seconds, automating tasks that would take days or longer for human beings to perform--and the public sector in the United States is at the very beginning of a long-term journey to develop and harness these tools. The National Academy of Public Administration identified Making Government AI Ready as one of the Grand Challenges in Public Administration. I chaired the Academy's Election 2020 Project Working Group on AI.

However, in this more recent test, General Atomics did develop additional algorithms for CODE to support "behavioral functions for a coordinated air-to-air search." During the demonstration, a human operator then instructed the Avenger and its five virtual wingmen to carry out the aerial search mission, which they then performed autonomously. The CODE "engine" flew the physical Avenger drone for more than two hours, according to the company's press release. It's interesting to note that the instructions from the human operator were sent to the drone using a Tactical Targeting Network Technology (TTNT) radio via the well-established Link16 waveform. The Navy developed TTNT first for the EA-18G Growler and it is now a key component of the service's Block III upgrade package for its F/A-18E/F Super Hornets.

Artificial intelligence promises to drive the growth of the United States economy and improve the quality of life of all Americans. On December 3, 2020, President Donald J. Trump signed the Executive Order on Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government, which establishes guidance for Federal agency adoption of Artificial Intelligence (AI) to more effectively deliver services to the American people and foster public trust in this critical technology. This order recognizes the potential for AI to improve government operations, such as by reducing outdated or duplicative regulations, enhancing the security of Federal information systems, and streamlining application processes. It also directs agencies to ensure that the design, development, acquisition, and use of AI is done in a manner that protects privacy, civil rights, civil liberties, and American values. The Executive Order (EO) underscores the Trump Administration's commitment to accelerating Federal adoption of AI, modernizing government, cultivating public trust in AI, and exemplifying world leadership in the use of trustworthy AI.

Supervised learning is, by far, the most researched approach. It is also divided into two major categories: Regression and Classification, with the data structure used to train these algorithms being the common denominator. You can think of them in the form of examples. With each of them, we get some features that pose a question, as well as the correct answer to this question, which we call the target. To better understand it, let us explore each category with an example.

The conversation around artificial intelligence and automation seems dominated by either doomsayers who fear robots will supplant all humans in the workforce, or optimists who think there's nothing new under the sun. But MIT Sloan professor Erik Brynjolfsson and his colleagues say that debate needs to take a different tone. New research finds that specific tasks within jobs, rather than entire occupations themselves, will be replaced by automation in the near future, with some jobs more heavily impacted than others. "Our findings suggest that a shift is needed in the debate about the effects of AI: away from the common focus on full automation of entire jobs and pervasive occupational replacement toward the redesign of jobs and reengineering of business practices," the researchers write in an article published in May in the American Economic Association Papers and Proceedings. The work is by Brynjolfsson, professor Tom Mitchell of Carnegie Mellon University's machine learning department, and Daniel Rock, a doctoral candidate and researcher at the MIT Initiative on the Digital Economy.

Over the last couple of years, it has become increasingly clear that many democratic governments have been taking a closer look at artificial intelligence (AI), both from a policy standpoint and as something to help their economies of the future. I specify democratic because of two reasons. First, it's clear that China recognized both the economic power and the population control capabilities of AI much earlier. Democracies have many open issues and can move more slowly, and policy is discussed more widely by the population. Two pieces of news this week have shown the increasing focus on AI in the United States and the European Union (EU).