No one doubts that our future will feature more automation than our past or present. The question is how we get from here to there, and how we do so in a way that is good for humanity. Sometimes it seems the most direct route is to automate wherever possible, and to keep iterating until we get it right. Here's why that would be a mistake: imperfect automation is not a first step toward perfect automation, anymore than jumping halfway across a canyon is a first step toward jumping the full distance. Recognizing that the rim is out of reach, we may find better alternatives to leaping--for example, building a bridge, hiking the trail, or driving around the perimeter. This is exactly where we are with artificial intelligence. AI is not yet ready to jump the canyon, and it probably won't be in a meaningful sense for most of the next decade. Rather than asking AI to hurl itself over the abyss while hoping for the best, we should instead use AI's extraordinary and improving capabilities to build bridges.

Media figures and Democrats warned women will face dire consequences if former President Trump is re-elected. Ohio senator and Republican candidate for vice president JD Vance took aim at Minnesota Gov. Tim Walz's football IQ after the Democratic vice presidential nominee posted a confusing tweet during a livestream of himself playing Madden. "They parade Tim Walz around as some kind of football genius as a former football coach, and maybe I know more about football than Gov. Tim Walz does," Vance said during a rally in Saginaw, Michigan on Tuesday. The comments come after Walz teamed up with Rep. Alexandria Ocasio-Cortez, D-N.Y., to livestream a session of the two playing the Madden NFL video game against each other, an event that was reportedly an effort by the campaign to widen its appeal among young male voters. Democratic vice presidential candidate Tim Walz addresses the crowd at a "Native Americans for Harris-Walz" event at MGM Grand in Las Vegas, Sunday, Oct. 27, 2024.

This summary highlights selected information about us and this offering appearing in this prospectus supplement, the accompanying prospectus and the documents incorporated or deemed incorporated by reference herein and therein. This summary may not contain all of the information that you should consider before making an investment decision. You should read carefully the more detailed information included or referred to under the heading "Risk Factors" of this prospectus supplement and the other information included in this prospectus supplement, the accompanying prospectus, the documents incorporated or deemed incorporated by reference herein and therein, including our Annual Report on Form 10-K for the year ended February 28, 2021, before deciding to invest in our common stock. Artificial Intelligence Technology Solutions Inc. or AITX is a Nevada corporation that, through its three subsidiaries, focuses on applying advanced artificial intelligence (AI)-driven technologies, paired with multi-use hardware and supported by custom software and cloud services, to intelligently automate and integrate a variety of high-frequency security, concierge, and operational tasks. As of December 28, 2021, RAD I, AITX's primary operating subsidiary, has 205 units deployed and approximately 50 units on backorder.

Awareness of artificial intelligence (AI) is increasing throughout health care. Relative to pharmacy, the American Society of Health-Systems Pharmacists Foundation's "Pharmacy Forecast 2020: Strategic Planning Advice for Pharmacy Departments in Hospitals and Health Systems" specifically lists the emergence of AI in its report, inspiring industry discussion and guiding the strategic planning processes of health system pharmacy leadership across the country.1 For pharmacy, AI provides information on drug interactions, drug therapy monitoring, formulary selection, costs, usage trends, and more. "AI is already transforming health care but will become increasingly valuable as investments in systems that can capture and manage data are made and clinical informatics entities work more collaboratively to address current data shortfalls," said Doug Zurawski, PharmD, senior vice president of clinical strategy at Kit Check, Inc, maker of radio frequency identification (RFID)/AI technology for hospital pharmacies to help with medication management. "It is incumbent upon us, in this industry and in this field, to take the lead and learn more, invest in systems that support AI and machine learning, and prepare for the future with access to artificial intelligence."

Debbie Vitany is fighting a losing battle against Fortnite. Her 17-year-old son, Carson, has been logging 12 hours a day on the video game, searching for weapons and resources in a post-apocalyptic world where the goal is being the last person standing. Teachers complain he falls asleep in class and his grades have plummeted. "We'd made some progress in getting him to cut down his Fortnite hours and get better sleep, but he's slipped back into his old habits," Ms Vitany, who lives near Saginaw, Michigan, said in an interview. "I've never seen a game that has such control over kids' minds."

Companies and cities all over world are experimenting with using artificial intelligence to reduce and prevent crime, and to more quickly respond to crimes in progress. The ideas behind many of these projects is that crimes are relatively predictable; it just requires being able to sort through a massive volume of data to find patterns that are useful to law enforcement. This kind of data analysis was technologically impossible a few decades ago, but the hope is that recent developments in machine learning are up to the task. There is good reason why companies and government are both interested in trying to use AI in this manner. As of 2010, the United States spent over $80 billion a year on incarations at the state, local, and federal levels. Estimates put the United States' total spending on law enforcement at over $100 billion a year. Law enforcement and prisons make up a substantial percentage of local government budgets.

We have adapted an expert system of medical diagnosis for use by low to mid-level health workers in remote and rural locations. Key to the successful deployment of this expert system is the rapid adaptation of the database and clinical interface for use in specific regions and by varying user skill.