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) …
Jeff Heepke knows where to plant corn on his 4,500-acre farm in Illinois because of artificial intelligence (AI). He uses a smartphone app called Climate Basic, which divides Heepke's farmland (and, in fact, the entire continental U.S.) into plots that are 10 meters square. The app draws on local temperature and erosion records, expected precipitation, soil quality, and other agricultural data to determine how to maximize yields for each plot. If a rainy cold front is expected to pass by, Heepke knows which areas to avoid watering or irrigating that afternoon. As the U.S. Department of Agriculture noted, this use of artificial intelligence across the industry has produced the largest crops in the country's history.
Digital transformation is a priority for the banking sector – and it represents both a challenge and an opportunity. Here are Synechron's top five areas that we believe banks will be thinking about and investing in this year from a technology point of view. Open Banking, OpenAPIs and'Banking as A Service'should disrupt traditional ways of working and drive adoption of digital banking services. Banks are now in the process of enabling each other and authorized third parties – including tech companies and fintechs- to have access to their customers' account transaction data with full read and write functionality. This could be the initiative that brings one or more of Google, Apple and Facebook in to transform banking as we know it, without having to'become' banks themselves.
People watch the intelligent service robot in the Wenling Branch of Agriculture Bank of China on August 28, 2016 in Wenling, Zhejiang Province of China. The intelligent service robot, 'Binbin,' can provide information service, autonomous navigation and guidance for requested positions and is capable of human-robot interaction. With decades of lab research, artificial intelligence (AI) technology is finally coming to its anticipated fruition. The time is right to open a Pandora's Box for laboratory AI, introducing it to solve real business needs and address real-world problems. It is also the time for some scientists in the field to consider starting their entrepreneurial journey.
Imagine a world where you can accelerate the sales pipeline, improve win rates and save time in the process. It sounds like a pipe dream. Artificial intelligence (AI) is making this a reality, upending the traditional sales process and transforming it for the better. There's a lot of fear out there around artificial intelligence – that it will replace the role of salespeople. AI shouldn't be looked at as an army of robots about to steal our sales jobs – it should be viewed as a resource that's going to give salespeople supercharged abilities.
THE SPANISH CITY OF BARCELONA plans to replace its Microsoft software with open source alternatives including Linux, Libre Office and Open Xchange. Barcelona plans to invest 70 per cent of its annual software budget in open source this year, according to El Pais, with the aim of completing the transformation by spring 2019. Microsoft's Outlook and Exchange Server email software is to be replaced by Open-Xchange, Microsoft Office will be ditched in favour of Libre Office, and Mozilla's Firefox will be made the default browser across systems. The city council has been piloting the use of Ubuntu Linux desktops for some time and it is likely that this distribution will be chosen as the operating system of choice. With this move, Barcelona becomes the first city to join an initiative by Free Software Foundation Europe dubbed'Public code, public money' which calls on public bodies to invest tax revenues in free reusable systems that are open to local businesses rather than proprietary licensed software.
Recently Google DeepMind program AlphaGo Zero achieved superhuman level without any help - entirely by self-play! Here is the Nature paper explaining technical details (also PDF version: Mastering the Game of Go without Human Knowledge) One of the main reasons for success was the use of a novel form of Reinforcement learning in which AlphaGo learned by playing itself. The system starts with a neural net that does not know anything about Go. It plays millions of games against itself and tuned the neural network to predict next move and the eventual winner of the games. The updated neural network was merged with the Monte Carlo Tree Search algorithm to create a new and stronger version of AlphaGo Zero, and the process resumed.
Imagine a business world where employees are faster and more productive – where they can make smarter decisions and have the time to focus on strategy and being creative. This is all a near-reality with continued breakthroughs in artificial intelligence (AI) capabilities. AI is at the tipping point of becoming the next great technological disruptor. Improvements in computing power, the advent of big data and breakthroughs in machine learning have created the ideal environment for AI to flourish and augment human potential. At the very least, AI will transform our economies, reshape consumer expectations, and increase the speed and scale of business.
The data researchers no longer depend only on interviews, surveys, observational studies to collect data. Instead, they have switched to the faster ways of data collection which includes leveraging internet, cameras, smartphones, drones, bots and many more. Later, the collected data is used by organization / governments to make business decisions. But, before that, they require a device or system which can store and secure such big data sets. One such system is Hadoop File Distribution System, commonly known as HDFS.
This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition. I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners -- we start off with the fundamentals of neural networks and machine learning and by the end of the program you're training state-of-the-art networks on the ImageNet dataset from scratch. Along the way I quickly realized that a stumbling block for many readers is configuring their development environment -- especially true for those wanted to utilize their GPU(s) and train deep neural networks on massive image datasets (such as ImageNet).
As global technology has evolved over the years, we have moved from television to the internet, and today we are smoothly and gradually adapting Artificial Intelligence. The term AI was first coined by John McCarthy in 1956. It involves a lot of the main things ranging from process automation of robotics to the actual process of robotics. It has become highly popular among large enterprises today owing to the amount of data these companies are dealing with. Increase in the demand for understanding the data patterns has led to the growth in demand of AI.