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) …
Jean-Michel Besnier is a French philosopher who teaches at Sorbonne University in Paris. His research focuses on the philosophical and ethical impact of science and technology on individual and collective representations and imagination. We met with him to talk about the consequences of the explosion of robotics and artificial intelligence (AI) in the healthcare sector, especially since the beginning of the Covid-19 pandemic. MedicalExpo e-magazine: Can you give us your definition of artificial intelligence? Jean-Michel Besnier: I have the same definition that everyone has. I am more attentive to the conceptual extension of the notion of artificial intelligence, which at the beginning referred to something rather simple, that is to say the implementation of devices capable of solving problems in an automatic or algorithmic way.
In recent years, computational tools based on reinforcement learning have achieved remarkable results in numerous tasks, including image classification and robotic object manipulation. Meanwhile, computer scientists have also been training reinforcement learning models to play specific human games and videogames. To challenge research teams working on reinforcement learning techniques, the Neural Information Processing Systems (NeurIPS) annual conference introduced the MineRL competition, a contest in which different algorithms are tested on the same task in Minecraft, the renowned computer game developed by Mojang Studios. More specifically, contestants are asked to create algorithms that will need to obtain a diamond from raw pixels in the Minecraft game. The algorithms can only be trained for four days and on 8,000,000 samples created by the MineRL simulator, using a single GPU machine.
Modern airports are using artificial intelligence (AI) to avoid mishandling baggage. The idea is to use AI for end-to-end tracking of baggage and planning optimized luggage routes, right from the time a passenger gets on-board, till she collects the baggage at the destination. Nearly all sectors of the economy - education, healthcare, finance, travel, and even the public sector - are applying technology to optimize their functioning. Technologies like AI are digitizing areas that were once thought capable of manual operations alone. Airports and airline companies are exploring the benefits of integrating technology into their operations and services.
A futuristic concept that has its roots dating back to the early 60s has been waiting for that one game-changing moment to become not just mainstream but inevitable as well. Yes, we are talking about the rise of Big Data and how this has made it possible for a highly complex concept like Artificial Intelligence (AI) to become a global phenomenon. This very fact should give us the hint that AI is incomplete or rather impossible without data and the ways to generate, store and manage it. And like all principles are universal, this is true in the AI space as well. For an AI model to function seamlessly and deliver accurate, timely, and relevant results, it has to be trained with high-quality data.
America is ahead of the game, but many other countries like China, India, Japan, Germany, England, Italy, France, and Russia are investing substantially in developing AI systems. Machine learning (ML) has prevailed and become a buzzword in the media. However, it is not AI itself. Another buzzword filling the columns in the media is deep learning. AI tools come with many bugs.
"We need to know how the many subjective decisions that go into building a model lead to the observed results, and why those decisions were thought justified at the time, just to have a chance at disentangling everything when something goes wrong," the paper reads. "Algorithmic impact assessments cannot solve all algorithmic harms, but they can put the field and regulators in better positions to avoid the harms in the first place and to act on them once we know more." A revamped version of the Algorithmic Accountability Act, first introduced in 2019, is now being discussed in Congress. According to a draft version of the legislation reviewed by WIRED, the bill would require businesses that use automated decision-making systems in areas such as health care, housing, employment, or education to carry out impact assessments and regularly report results to the FTC. A spokesperson for Senator Ron Wyden (D-Ore.), a cosponsor of the bill, says it calls on the FTC to create a public repository of automated decision-making systems and aims to establish an assessment process to enable future regulation by Congress or agencies like the FTC.
The system is now advanced in accuracy. Roboat II autonomously navigated the canals of Amsterdam for three hours collecting data and returned back to its start location with an error margin of only 0.17 meters, or fewer than 7 inches. There are now advanced navigation and control algorithms for communication and collaboration between boats. The system is modeled on an ant colony using a distributed controller. In this model, there is no direct communication among the connected robots -- only one leader knows the destination.
Recruiting is a top concern for enterprises in 2021. In a survey by XpertHR, roughly one-half of responding employers plan to increase their workforce in 2021, but expect that hurdles will stand in the way. A high volume of low-quality applicants is stymying the search for the ideal candidates, with one source pegging the average number of unqualified applicants at 75%. Even among those that do make it through the recruiting funnel, a significant portion ultimately change their minds -- exacerbating the recruiting challenge. Against this backdrop, Sense, an "AI-driven" talent engagement and communications platform, today announced that it raised $50 million in series D funding led by SoftBank.
In reinforcement learning, an artificial intelligence faces a game-like situation. The computer employs trial and error to come up with a solution to the problem. This is the most complete Reinforcement Learning course on Udemy. In it you will learn the basics of Reinforcement Learning, one of the three paradigms of modern artificial intelligence. You will implement from scratch adaptive algorithms that solve control tasks based on experience.
In an interview, Rajeev Rastogi, vice-president of ML at Amazon India, said the company has developed computer vision programs that recognize defects such as cuts and scratches on tomatoes and onions to figure out when they have gone bad. The system uses a mix of convolutional neural networks (CNNs) and visual transformer (ViTs) algorithms. CNNs are deep learning algorithms that can take image input and assign importance to various aspects of that image, while ViTs are specialized versions of transformer algorithms, which can weigh the significance of each part of data it gets. "In our grocery business, produce quality is the single-most important customer input and the number one driver of repeat purchase," Rastogi said. "Currently, quality is processed manually, which doesn't really scale. It's also very error-prone, is costly and doesn't have high repeatability. So, we developed a computer vision system for grading fresh produce quality by analysing images of produce," he said.