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) …
AI is transforming all business functions, and software development is no exception. Not only can machine learning techniques be used to accelerate the traditional software development lifecycle (SDLC), they present a completely new paradigm for inventing technology. Traditionally, developing a computer programs requires you to specify in advance exactly what you want the system to do and then hand engineer all of the features of your technology. Encoding many tasks in an explicit way is possible, as computers before the advent of AI were still quite powerful. There are many tasks and decisions, however, that are far too complex to teach to computers in a rigid, rule-based way.
Bootcamp Description Everything we have learnt in software development is undergoing change. Thousands of job go unfilled at various companies as they can't find qualified engineers who know machine learning. TiE's one day bootcamp will set you on the right path. This is one investment in your career you can't afford to miss. Whether you are starting out or want to manage a team of machine learning experts, here is your chance to take just one day from your schedule and walk with real hands on experience with machine learning concepts.
At LG we make products and services that make lives better, easier and happier through increased functionality and fun. Put simply, we offer the latest innovations to make "Life Good" – from home appliances, consumer electronics, vehicle components and mobile communications to business innovations in digital signage, air conditioning, solar and LED lighting. As a global leader, we strive for greatness in product leadership, market leadership and people leadership to realize our growth strategies. LG's Silicon Valley Labs (SVL) is looking for passionate and talented computer vision scientists and AI engineers to work closely with the software teams at SVL on novel and disruptive Vision based product and platform solutions that work for real customers. These vision based solutions coming out of LG Silicon Valley Labs will be an important component of LG's global vision for Automotive, IoT, Robotics and connected services.
In the previous blogs, I have been talking about what artificial intelligence (AI) is and some of the hype and myths behind it. One of the observations was that for me, for IFS, AI is very much a means to a goal. It's about applying to a very specific use case to deliver better results. One of these use cases is about workforce optimization. For more than 12 years, IFS has been using machine learning algorithms and other smart techniques inside IFS Planning & Scheduling Optimization (PSO) to provide optimized schedules for our customers.
Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.
Deep Learning enjoys its current hype for four main reasons. These are data, computational power, the algorithms itself and marketing. We will discuss each of them in the following sections. One of the things that increased the popularity of Deep Learning is the massive amount of data that is available in 2018, which has been gathered over the last years and decades. This enables Neural Networks to really show their potential since they get better the more data you fed into them.
And just like that, humanity draws one step closer to the singularity, the moment when the machines grow so advanced that humans become obsolete: A robot has learned to autonomously assemble an Ikea chair without throwing anything or cursing the family dog. Researchers report today in Science Robotics that they've used entirely off-the-shelf parts--two industrial robot arms with force sensors and a 3-D camera--to piece together one of those Stefan Ikea chairs we all had in college before it collapsed after two months of use. From planning to execution, it only took 20 minutes, compared to the human average of a lifetime of misery. It may all seem trivial, but this is in fact a big deal for robots, which struggle mightily to manipulate objects in a world built for human hands. To start, the researchers give the pair of robot arms some basic instructions--like those cartoony illustrations, but in code.
Over the last few years machine learning has become embedded in a wide variety of day-to-day business, nonprofit, and government operations. As the popularity of machine learning increased, a cottage industry of high-quality literature that taught applied machine learning to practitioners developed. This literature has been highly successful in training an entire generation of data scientists and machine learning engineers. This literature also approached the topic of machine learning from the perspective of providing a learning resource to teach an individual what machine learning is and how it works. However, while fruitful, this approach left out a different perspective on the topic: the nuts and bolts of doing machine learning day to day.
Last year Google partnered with the Raspberry Pi Foundation to survey users on what would be most helpful in bringing Google's artificial intelligence and machine learning tools to the Raspberry Pi. Now those efforts are paying off. Thanks to Colaboratory – a new open-source project from Google – engineers, researchers, and makers can now build and run machine learning applications on a simple single-board computer. Google has officially opened up its machine learning and data science workflow – making learning about machine learning or data analytics as easy as using a notebook and a Raspberry Pi. Google's Colaboratory is a research and education tool that can easily be shared via Google's Chrome web browser.