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) …
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.
If your work puts you in regular contact with technology vendors, you'll have heard terms such as artificial intelligence (AI), machine learning (ML), natural language processing and computer vision before. You'll have heard that AI/ML is the future, that the boundaries of these technologies are constantly being pushed and broadened, and that AI/ML will play an integral role in shaping this tech-forward era's most successful business models. As a technology leader, I've heard all these claims and more. To say that AI/ML will play an increasingly impactful role in business is no overstatement. According to a recent Forbes article, the machine learning market is poised to more than quadruple in the coming years.
"I would say everyone has read at least once an algorithmically produced article," said Robert Weissgraeber, CTO and Managing Director of AX Semantics. In many cases, readers don't see a difference between human- and bot-authored copy, Weissgraeber told Built In. His company, AX Semantics, is one of several -- including Narrative Science and Automated Insights -- exploring natural language generation, or automated writing. The technology can be used to generate product descriptions, quarterly earnings reports, fantasy football recaps and journalism. The Washington Post, for instance, has developed an AI-enabled bot, Heliograf, that helps generate election and sports coverage.
Mobileye, Intel's driverless vehicle R&D division, today published a 40-minute video of one of its cars navigating a 160-mile stretch of Jerusalem streets. The video features top-down footage captured by a drone, as well as an in-cabin cam recording, parallel to an overlay showing the perception system's input and predictions. The perception system was introduced at the 2020 Consumer Electronics Show and features 12 cameras, but not radar, lidar, or other sensors. Eight of those cameras have long-range lenses, while four serve as "parking cameras" and all 12 feed into a compute system built atop dual 7-nanometer data-fusing, decision-making Mobileye EyeQ5 chips. Running on the compute system is an algorithm tuned to identify wheels and infer vehicle locations and an algorithm that identifies open, closed, and partially open car doors.
But we aren't talking about whether hardware has gotten bigger or better at executing AI algorithms. We're talking about the underlying algorithms themselves and how much complexity is useful in an AI model. I've actually been learning something about this topic directly; my colleague David Cardinal and I have been working on some AI-related projects in connection to the work I've done with the DS9 Upscale Project. Fundamental improvements to algorithms are difficult and many researchers aren't incentivized to fully test if a new method is actually better than an old one -- after all, it looks better if you invent an all-new method of doing something rather than tuning something someone else created.
'Passive' visual experiences play a key part in our early learning experiences and should be replicated in AI vision systems, according to neuroscientists. Italian researchers argue there are two types of learning – passive and active – and both are crucial in the development of our vision and understanding of the world. Who we become as adults depends on the first years of life from these two types of stimulus – 'passive' observations of the world around us and'active' learning of what we are taught explicitly. In experiments, the scientists demonstrated the importance of the passive experience for the proper functioning of key nerve cells involved in our ability to see. This could lead to direct improvements in new visual rehabilitation therapies or machine learning algorithms employed by artificial vision systems, they claim.
Facebook executives took the decision to end research that would make the social media site less polarising for fears that it would unfairly target right-wing users, according to new reports. The company also knew that its recommendation algorithm exacerbated divisiveness, leaked internal research from 2016 appears to indicate. Building features to combat that would require the company to sacrifice engagement – and by extension, profit – according to a later document from 2018 which described the proposals as "antigrowth" and requiring "a moral stance." "Our algorithms exploit the human brain's attraction to divisiveness," a 2018 presentation warned, warning that if action was not taken Facebook would provide users "more and more divisive content in an effort to gain user attention & increase time on the platform." According to a report from the Wall Street Journal, in 2017 and 2018 Facebook conducted research through newly created "Integrity Teams" to tackle extremist content and a cross-jurisdictional task force dubbed "Common Ground."
The healthcare space is growing by leaps and bounds. According to a report, global healthcare expenditure is expected to reach USD 10 trillion by 2022. Owing to multiple factors like technological advancements, expensive infrastructure, growing health-related awareness, and a rise in chronic health conditions, the healthcare market is evolving faster than ever. With time, the use of technology has brought structural changes to the healthcare industry, for the better. Whether it's managing endless administrative processes in hospitals, providing personalized care and treatment, or facilitating better access, technological advancements like mobile healthcare, also known as mHealth, and machine learning in healthcare have streamlined the healthcare sector to a great extent.
This post is a part of a medium based'A Layman's guide to Deep Learning' series that I plan to publish in an incremental fashion. The target audience is beginners with basic programming skills; preferably Python. This post assumes you have a basic understanding of Deep Neural Networks a.k.a. A detailed post covering this has been published in the previous post -- A Layman's guide to Deep Neural Networks. Reading the previous post is highly recommended for a better understanding of this post. 'Computer Vision' as a field has evolved to new heights with the advent of deep learning.
AI has become the need of the hour and all the industries are now integrating analytics and AI to drive the decision-making process. Bhagirath Kumar Lader, who is the Chief Manager (Business Information System) at GAIL led us through a session briefing Artificial Intelligence essentials for business leaders in today's age. Lader is one of the key members of the digital transformation team at GAIL and carries huge knowledge about how AI, ML and DL are crucial to businesses. He gave us a quick overview of the motivation for AI, AI essentials, AI hype vs reality while taking us through use cases. While AI is a crucial part of businesses, one of the key drivers of its implementation is its ability to make the decision which is usually considered the forte of humans.