A thousand ways to deploy Machine learning models - A.P.I

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"What use is a machine learning model if you don't deploy to production " -- Anonymous You have done a great work building that awesome 99% accurate machine learning model but your work most of the time is not done without deploying. Most times our models will be integrated with existing web apps, mobile apps or other systems. How then do we make this happen? I said a thousand, I guess I have just a few. I am guessing you would have found the right one for you before you get past the first two or three.


MNIST Database

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In simple terms, MNIST can be thought of as the "Hello, World!" of machine learning. MNIST is primarily used to experiment with different machine learning algorithms and to compare their relative strengths. Yann LeCun, one of the three researchers behind the creation of MNIST, has devoted a portion of his research to using MNIST to experiment with cutting edge algorithms, which can be seen on his personal website yann.lecun.com. Many researchers, hobbyists, and students alike continue to use MNIST alongside their algorithmic implementations and other popular datasets as a way to solidify their understanding of the fundamental concepts in machine learning and to compare their new algorithms against existing cutting edge research.


Artificial Intelligence In Denver - Inside Out Perspective

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Artificial Intelligence (AI) is a computer or electronic device performing actions as if it were a human – it would apply some sort of intelligence factor or representation to accomplish the task. Some of these human services these electronic devices are performing include different planning methods and actions that include learning, problem solving, motion, thought manipulation, social response and intelligence, creativity, knowledge representation, and imitation. These electronic manipulations happening occur simultaneously with our daily lives, most of the time without us even realizing. Different examples of frequently used AI programming include virtual assistants (like Amazon's Alexa or Apple's Siri) photo recognition (like on social platforms and personal devices), and spam and credit card fraud testing; as well as more in-depth projects, like self-driving cars, check-out kiosks, and recommendation engines that frequent your past purchases to create their own ads. As consumers and participants in a fast-paced electronically changing world, we have not only let these AI infiltrations become a part of our daily lives, but we also have not educated ourselves on their pros and cons.


Artificial Intelligence: 101 Things You Must Know Today About Our Future: Lasse Rouhiainen: 9781982048808: Amazon.com: Books

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Lasse Rouhiainen is a best-selling author and international expert on artificial intelligence, disruptive technologies and digital marketing. Finnish in origin but based in Spain, Lasse focuses his work on investigating how companies and society in general can better adapt to, and benefit from, artificial intelligence. Lasse has given keynote presentations, seminars and workshops in more than 16 countries around the world and holds frequent conferences at several universities internationally. He has also provided training to thousands of students and businesses through online e-learning courses. Lasse has been a speaker at renowned seminars such as Mobile World Capital and TEDx, and has worked with top brands and institutions such as Michelin, Össur and the European Union Intellectual Property Office.


How Privacy Trends Will Shape the Next Decade of IoT - RTInsights

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The governing regulations over the use of data and who has access to it will change the landscape of how we move about in the online world. Over the past decade, data has emerged as "the new oil" – a driving force behind the world's economy. Because of the sheer amount of data, new concerns for its use have driven innovation within the privacy and security sphere. Let's take a look at how some of these situations will shape what we understand of privacy and how it appears in industry use cases. Widespread consensus with artificial intelligence is that people still don't trust AI.


Why AI companies don't always scale like traditional software startups

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At a technical level, artificial intelligence seems to be the future of software. AI is showing remarkable progress on a range of difficult computer science problems, and the job of software developers – who now work with data as much as source code – is changing fundamentally in the process. Many AI companies (and investors) are betting that this relationship will extend beyond just technology – that AI businesses will resemble traditional software companies as well. Based on our experience working with AI companies, we're not so sure. We are huge believers in the power of AI to transform business: We've put our money behind that thesis, and we will continue to invest heavily in both applied AI companies and AI infrastructure. However, we have noticed in many cases that AI companies simply don't have the same economic construction as software businesses. At times, they can even look more like traditional services companies.


How Google used AI to supercharge Maps in 2019

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Google Maps celebrated its 15th birthday today by announcing a new milestone: in the last year, the company mapped as many buildings as it did in the previous decade. The service reached this landmark through a two-step process. Firstly, staff worked with Google's data operations team to manually trace common building outlines. They then trained machine learning models to recognize the edges and shapes of buildings. Another recent deployment of machine learning enabled Maps to recognize handwritten building numbers that were so unclear that even a passerby in a car couldn't see them.


The top qualification for an AI governance officer? Courage

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Fast Company: What does the term "responsible AI" mean to you? Cathy Bessant: When people hear it or hear me talk about the risks, there's a misperception that I think AI is something to be avoided. AI is going to drive a huge amount of growth. That said, the legal, social, ethical, the framework around AI really doesn't exist. If your model gets something wrong, who do you blame? The person who created the model, the company that sold it to you . . .


Artificial Intelligence Used to Supercharge Battery Development for Electric Vehicles

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Using machine learning, a Stanford-led research team has slashed battery testing times – a key barrier to longer-lasting, faster-charging batteries for electric vehicles. Using a new machine learning method, a Stanford-led research team has slashed battery testing times – a key barrier to longer-lasting, faster-charging batteries for electric vehicles – by nearly fifteenfold. Battery performance can make or break the electric vehicle experience, from driving range to charging time to the lifetime of the car. Now, artificial intelligence has made dreams like recharging an EV in the time it takes to stop at a gas station a more likely reality, and could help improve other aspects of battery technology. For decades, advances in electric vehicle batteries have been limited by a major bottleneck: evaluation times.


Global Economy Implications of Artificial Intelligence

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Artificial intelligence can possibly gradually add 16% or around US$13 trillion by 2030 to current worldwide economic output – an annual average contribution to efficiency development of about 1.2% between now and 2030, as indicated by a September, 2018 report by the McKinsey Global Institute on the impact of AI on the world economy. The McKinsey report depends on simulation models of the impact of Artificial Intelligence at the nation, sector, organization and worker levels. It took a look at their adoption of five general classifications of AI technologies: computer vision; natural language; virtual assistants, robotic process automation, and advanced machine learning. Data sources included survey information from around 3,000 firms in 14 distinct parts and economic information from various organizations including the United Nations, the World Bank and the World Economic Forum. Artificial intelligence (AI) and machine learning (ML) are being embraced by a more prominent number of people, organizations, and governments as rising effectiveness and productivity are allowing exponential growth in specific parts of the worldwide economy.