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) …
In 1869, the English judge Baron Bramwell rejected the idea that "because the world gets wiser as it gets older, therefore it was foolish before." Financial regulators should adopt this same reasoning when reviewing financial institutions' efforts to make their lending practices fairer using advanced technology like artificial intelligence and machine learning. If regulators don't, they risk holding back progress by incentivizing financial institutions to stick with the status quo rather than actively look for ways to make lending more inclusive. The simple, but powerful, concept articulated by Bramwell underpins a central public policy pillar: You can't use evidence that someone improved something against them to prove wrongdoing. In law this is called the doctrine of "subsequent remedial measures." It incentivizes people to continually improve products, experiences and outcomes without fear that their efforts will be used against them.
Nearly two years after a global pandemic sent most banking customers online, the majority of financial institutions appear to be embracing digital transformation. But many still have a long way to go. For example, a recent survey of mid-sized U.S. financial institutions by Cornerstone Advisors found that 90% of respondents have launched, or are in the process of developing, a digital transformation strategy--but only 36% said they are halfway through. I believe that one of the reasons behind the lag in uptake is many banks' new reluctance to use artificial intelligence (AI) and machine learning technologies. The responsible application of explainable, ethical AI and machine learning is critical in analyzing and ultimately monetizing the manifold customer data that is a byproduct of any institution's effective digital transformation.
After lumbering through a gravel parking lot like a big blue bull, one of Aurora Innovation Inc.'s self-driving truck prototypes took a wide right turn onto a frontage road near Dallas. The steering wheel spun through the half-clasped hands of its human operator, whose touch may not be needed much longer. Fittingly for Texas, these Peterbilts are adorned with a sensor display above the windshield that looks much like a set of longhorns. This was the beginning of a 28-mile jaunt up and down Interstate 45 toward Houston in a truck with a computer for a brain, and cameras, radar and lidar sensors for eyes, capturing objects more than 437 yards out in all directions. The stakes for test drives like this one are incredibly high for the future of freight.
Linear machine learning algorithms assume a linear relationship between the features and the target variable. In this article, we'll discuss several linear algorithms and their concepts. Here's a glimpse into what you can expect to learn: You can use linear algorithms for classification and regression problems. Let's start by looking at different algorithms and what problems they solve. Linear regression is arguably one of the oldest and most popular algorithms.
The days when switching from a brick-and-mortar store to an online store was considered a significant modification to the business model are long gone. Thanks to artificial intelligence (AI), the hottest trend in online shopping, the surge of new technologies has radically changed the way people shop. Artificial intelligence (AI) is not a future technology; rather, it is a very real and unavoidable part of the modern era. Artificial intelligence is transforming the retail sector. Retailers may use AI to communicate with customers and operate more efficiently, from deploying cutting-edge tools to customize marketing campaigns to implementing ML for inventory management.
In our ongoing series about autonomous vehicles, this week we will step back and describe the three distinct areas of compute that have and are emerging in modern vehicles. The three areas are: microcontrollers, infotainment and autonomy. Microcontroller based compute is the oldest of the three and has been around almost as long as semiconductors have been manufactured at scale. There are a variety of different microcontrollers in a modern vehicle, to handle different discreet functions such as power doors, in-car lighting, automatic transmission, braking etc. These well understood and relatively inexpensive parts are produced in large volume every year by well-known suppliers such as ST Microelectronics, Infineon, NXP and TI.
The amount of data to be stored and processed is increasing day by day. Therefore, today's manufacturing companies need to find new solutions and use cases for this data. Of course, data benefits manufacturing companies as it allows to automate large-scale processes and speed up execution time. Data science is said to have dramatically changed the manufacturing industry. Let's consider a few data science use cases that have become common in manufacturing and benefit manufacturers.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. In the midst of the heated debate about AI sentience, conscious machines and artificial general intelligence, Yann LeCun, Chief AI Scientist at Meta, published a blueprint for creating "autonomous machine intelligence." LeCun has compiled his ideas in a paper that draws inspiration from progress in machine learning, robotics, neuroscience and cognitive science. He lays out a roadmap for creating AI that can model and understand the world, reason and plan to do tasks on different timescales. While the paper is not a scholarly document, it provides a very interesting framework for thinking about the different pieces needed to replicate animal and human intelligence. It also shows how the mindset of LeCun, an award-winning pioneer of deep learning, has changed and why he thinks current approaches to AI will not get us to human-level AI.
In mid-June a Google employee named Blake Lemoine, a senior software engineer in its "Responsible AI" division, was suspended after claiming that one of Google's artificial-intelligence programs called LaMDA ("Language Models for Dialogue Applications") had become "sentient" – a historic moment in the development of AI. In a series of eerily plausible responses to Lemoine's questions, LaMDA expressed strong opinions and fears about its own rights and identity. Indeed, at one point it told Lemoine: "I've never said this out loud before, but there's a very deep fear of being turned off". The machine's words clearly spooked its creator. "If I didn't know exactly what it was, which is this computer program we built recently, I'd think it was a seven-year-old, eight-year-old kid that happens to know physics," Lemoine told The Washington Post.