If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Ransomware will inevitably plague self-driving cars. Ransomware is being continually mentioned in the daily news and appears to be a seemingly unstoppable fiendish craze. Perhaps the recent attack of ransomware on the Colonial Pipeline received the most rapt attention since it led to concerns over gasoline shortages and caused quite a stir amongst the general public. When ransomware is used against a particular bank or hospital or school, this normally doesn't have quite the same widespread disruption as did the fuel pipeline incident. The thing is, we are probably going to see a lot more ransomware being fielded and doing so against all manner of businesses and governmental entities. Some would assert that we are only so far at the tip of the iceberg when it comes to ransomware hacks. Part of the reason why you can expect more use of ransomware is that it is relatively easy for an evildoer or crook to deploy the computer hacking scourge. Whereas the perpetrator used to need to have some keen computer nerdish skills, that's pretty much not the case anymore. Sadly, the ease of attempting to infect computer systems with ransomware has become nearly easy-peasy and has opened the floodgates to just about any determined villain to try (ransomware programs can be cheaply purchased online via the so-called dark web). There are now plentiful Ransomware-as-a-Service (RaaS) capabilities available that will do most of the heavy lifting for those that prefer a hands-off chauffeured form of ransomware cyberattacks.
FedEx is expanding its robotics testing to include one of the bigger names in autonomous delivery. The company has struck a multi-year deal with Nuro to test its self-driving delivery vehicles, including for "last-mile" deliveries. The team-up started this April with a Houston-area pilot, but that's likely to expand when Nuro characterized this as a pledge to use driverless vehicles on a "large-scale." This is a big move for Nuro. For FedEx, this could help it manage capacity, tackle less-than-ideal routes and cut costs (which, let's be honest, could involve job cuts). It's also a competitive play -- rivals like UPS are already testing self-driving trucks, and this could help it keep up as the courier business becomes increasingly automated.
Nuro's driverless bots are delivering FedEx packages in a city that already has autonomous pizza delivery. In Houston, Nuro's steering wheel-less delivery vehicles have been working alongside FedEx's traditional human-driven delivery fleet since April. The pilot program was first announced Tuesday, with plans to expand from testing to a bigger, more established deployment. Eventually the bot-delivered packages could reach other cities. Nuro wouldn't disclose how many Nuro delivery bots are roaming around several Houston ZIP codes, but the testing is part of FedEx's "last-mile" plans for home delivery.
In April, the European Commission released a wide-ranging proposed regulation to govern the design, development, and deployment of A.I. systems. The regulation stipulates that "high-risk A.I. systems" (such as facial recognition and algorithms that determine eligibility for public benefits) should be designed to allow for oversight by humans who will be tasked with preventing or minimizing risks. Often expressed as the "human-in-the-loop" solution, this approach of human oversight over A.I. is rapidly becoming a staple in A.I. policy proposals globally. And although placing humans back in the "loop" of A.I. seems reassuring, this approach is instead "loopy" in a different sense: It rests on circular logic that offers false comfort and distracts from inherently harmful uses of automated systems. A.I. is celebrated for its superior accuracy, efficiency, and objectivity in comparison to humans.
Artificial intelligence (AI) may be the most disruptive of all the disruptive technologies. At the very least, AI's depth and rapid evolution are fast it making it a foundation in myriad industries – a status that carries with it an assortment of investment implications. A plethora of exchange traded funds offer AI exposure in varying forms, but one of the dominant forces in that group is the ARK Autonomous Technology & Robotics ETF (CBOE: ARKQ). The actively managed ARKQ isn't a dedicated AI fund, but it features exposure to industries AI intersects with, including 3D printing, autonomous transportation, energy storage, robotics, and space exploration. As is the case with so many disruptive technologies, hardware and semiconductors are the backbones of AI, and that's not going to change anytime soon.
With ambitions to establish a network of autonomous trucking routes across the US, transport startup TuSimple is taking some steady and significant steps forward as it proves its technology through trials and expands into Europe. The latest test run for its self-driving trucks involved hauling a load of fresh produce over hundreds of miles across the US, where it demonstrated that it can complete such tasks in a fast and highly efficient fashion. Previously, we've seen TuSimple's Level 4 autonomous trucks use its variety of cameras and sensors to move goods as part of trials for the US Postal Service and shipping giant UPS. This time around, the startup has partnered with fresh produce provider The Giumarra Companies and Associated Wholesale Grocers to explore autonomous trucking's potential in the fresh food industry. The trial started in Nogales, Arizona, where TuSimple's truck was loaded up with fresh watermelons from Giumarra's facility.
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Mark is the CEO of W3.Digital, a Digital Transformation focused consultancy. There have been a few moments in history where technological advances and market forces combine to change the global economy's functions. These moments bypass incremental changes and create significant leaps that spur new businesses and sector-wide disruption. Railways, electricity power grids, telephony, air travel, early-stage computing, the internet and the advent of the mobile phone -- followed by the now-ubiquitous smartphone -- are examples of such leaps. While each of these moments was messy as they first emerged, each rapidly matured to a state where we cannot imagine life without them.
The automation industry is experiencing an explosion of growth and technology capability. To explain complex technology, we use terms such as "artificial intelligence" to convey the idea that solutions are more capable and advanced than ever before. If you are an investor, business leader, or technology user who seeks to understand the technologies you are investing in, this article is for you. What follows is an explanation of vision-guided robotics and deep-learning algorithms. That's right, the article is titled "artificial intelligence" and yet by the end of the first paragraph, we've already switched to deep-learning algorithms!
The Covid-19 pandemic was devastating for many industries, but it only accelerated the use of artificial intelligence across the U.S. economy. Amid the crisis, companies scrambled to create new services for remote workers and students, beef up online shopping and dining options, make customer call centers more efficient and speed development of important new drugs. Even as applications of machine learning and perception platforms become commonplace, a thick layer of hype and fuzzy jargon clings to AI-enabled software.That makes it tough to identify the most compelling companies in the space--especially those finding new ways to use AI that create value by making humans more efficient, not redundant. With this in mind, Forbes has partnered with venture firms Sequoia Capital and Meritech Capital to create our third annual AI 50, a list of private, promising North American companies that are using artificial intelligence in ways that are fundamental to their operations. To be considered, businesses must be privately-held and utilizing machine learning (where systems learn from data to improve on tasks), natural language processing (which enables programs to "understand" written or spoken language) or computer vision (which relates to how machines "see"). AI companies incubated at, largely funded through or acquired by large tech, manufacturing or industrial firms aren't eligible for consideration. Our list was compiled through a submission process open to any AI company in the U.S. and Canada. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). Forbes received several hundred entries, of which nearly 400 qualified for consideration. From there, our data partners applied an algorithm to identify 100 companies with the highest quantitative scores--and that also made diversity a priority. Next, a panel of expert AI judges evaluated the finalists to find the 50 most compelling companies (they were precluded from judging companies in which they have a vested interest). Among trends this year are what Sequoia Capital's Konstantine Buhler calls AI workbench companies--building of platforms tailored to different enterprises, including Dataiku, DataRobot Domino Data and Databricks.