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) …
A few years ago, when I was still working for IBM, I managed an AI project for a bank. During the final phase, my team and I went to the steering committee to present the results. Proud as the project leader, I have shown that the model has achieved 98 percent accuracy in detecting fraudulent transactions. In my manager's eyes, I could see a general panic when I explained that we used an artificial neural network, that it worked with a synapse system and weight adjustments. Although very efficient, there was no way to understand its logic objectively. Even if it was based on real facts, this raw explanation conditioned the project's continuity at that time, unless we could provide a full explanation that the senior executive could understand and trust.
Tata Steel is one of the prominent names in the steel-making industry boasting over three decades of manufacturing expertise. The company is currently the world's second-most geographically-diversified steel producer, with fully integrated operations -- from mining to the manufacturing and marketing of finished products. To sustain its leadership position in a volatile market, Tata Steel needed to fortify its supply chain. Poor visibility of in-plant operations was causing delays in loading trucks. This, in turn, triggered a series of inefficiencies like traffic congestions, parking problems, and forced route diversions for inbound/outbound vehicles.
Today, we are thrilled to announce a brand-new, game-changing product in our application-development suite. HCL Volt MX, an industry-leading low-code platform for developers, lets you build multiexperience consumer-grade apps rapidly and empowers you to deliver highly contextualized experiences to reach your customers, employees, and partners with the right information, in the right way, at the right time. Using low-code approaches, Volt MX provides the agility to create fast solutions and unified experiences across all channels -- as well as improves developer productivity, creates better app experiences more cost effectively, and helps you build innovative experiences that meet the evolution of customer expectations. From native mobile to PWAs to wearables, build once and deploy any app, anywhere -- even on kiosks! Your internal dev teams don't have to become experts on iOS, Android, or any available platform.
Market Study Report has recently added a report on Deep Learning Market which provides a succinct analysis of the market size, revenue forecast, and the regional landscape of this industry. The report also highlights the major challenges and current growth strategies adopted by the prominent companies that are a part of the dynamic competitive spectrum of this business sphere. The deep learning market has been segmented on the basis of offerings, applications, end-user industries, and geographies. In terms of offerings, software holds the largest share of the deep learning market. Also, the market for services is expected to grow at the highest CAGR from 2018 to 2023.
Scientists studying the movement of animals have longed for a motion-capture method similar to the one Hollywood animators use to create spectacular big-screen villains (think Thanos in "The Avengers"). Now a team of Harvard-led scientists has made a breakthrough, assembling a new system combining motion capture and deep learning to continuously track the 3D movements of freely behaving animals. The project, which monitors how the brain controls behavior, has the potential to help combat human disease or advance the creation of artificial intelligence. The system, called continuous appendicular and postural tracking using retroreflector embedding -- CAPTURE, for short -- delivers what's believed to be an unprecedented look at how animals move and behave naturally. This can one day lead to new understandings of how the brain functions.
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The ideal candidate will be responsible for using NLP and ML techniques to bring order to unstructured data. He/she should have experience in the latest techniques in AI, NLP, machine learning, including Deep Learning approaches. The aspirant will work within the engineering team to design, code, train, test, deploy and iterate on enterprise-scale machine learning systems.
The concept of data streaming is not new. But one of the most critical emerging uses for streaming data is in the public sector, where government agencies are eyeing its game-changing capability to advance everything from battlefield decision-making to constituent experience. IDC predicts that the collective sum of the world's data will grow 33%, to 175 zettabytes, by 2025. For context, at today's average internet connection speeds, 175 zettabytes would take 1.8 billion years for one person to download. Streaming has only further accelerated the velocity of data growth.
MILAN – Perhaps no single aspect of the digital revolution has received more attention than the effect of automaton on jobs, work, employment, and incomes. There is at least one very good reason for that – but it is probably not the one most people would cite. Former US President Donald Trump is not Hitler, and America is not the Weimar Republic. But, as four excellent recent books about the interwar years show, false narratives and craven political choices can have dreadful consequences that may not emerge immediately. Using machines to augment productivity is nothing new.
Obtaining historical data on the stocks that we want to observe is a two-step process. The library get-all-tickers allows us to compile a list of stock tickers by filtering companies on aspects like market cap or exchange. For this example, I am looking at companies that have a market cap between $150,000 and $10,000,000 (in millions). You will notice that I also included a line of code to print the number of tickers we are using. You will need to be sure that you are not targeting more than 2,000 tickers, because the Yfinance API has a 2,000 API calls per hour limit.