Few trends in technology have caused the level of panic and uncertainty in the job market as artificial intelligence (AI). The impending "robot revolution" has brought questions about what jobs, if any, will be replaced by bots, and when it will happen. While some have posited that robots will replace nearly all jobs, and free up humans to work on more creative endeavors, others have been more reserved in their predictions. A new report for Forrester Research claims that, by 2021, "intelligent agents and related robots" will only have eliminated 6% of jobs. "By 2021, AI within intelligent agents will evolve significantly beyond today's relatively simple machine learning and natural language processing (NLP)," the report said.

Smart technology has made life so much easier in recent years. It's made homes safer, it's made meals tastier, and it's made watches a whole lot more complicated. Unfortunately, there is no device that can work out for you. There's no app that can motivate you to run, and there's no wearable that will give you abs. Working out is still something you need to do on your own.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

In this special guest feature, Rob Farber from TechEnablement writes that modernized code can deliver significant speedups on machine learning applications. Benchmarks, customer experiences, and the technical literature have shown that code modernization can greatly increase application performance on both Intel Xeon and Intel Xeon Phi processors. Colfax Research recently published a study showing that image tagging performance using the open source NeuralTalk2 software can be improved 28x on Intel Xeon processors and by over 55x on the latest Intel Xeon Phi processors (specifically an Intel Xeon Phi processor 7210). For the study, Colfax Research focused on modernizing the C-language Torch middleware while only one line was changed in the high-level Lua scripts. NeuralTalk2 uses machine learning algorithms to analyze real-life photographs of complex scenes and produce a correct textual description of the objects in the scene and relationships between them (e.g., "a cat is sitting on a couch", "woman is holding a cell phone in her hand", "a horse-drawn carriage is moving through a field", etc.) Captioned examples are show in the figure below.

According to the keynotes delivered during several developer conferences over the past year, three key areas companies are looking to lead the technology industry in the future include a focus on machine learning, artificial intelligence and speech recognition. Ideally, the avenues of machine learning, speech recognition, and artificial intelligence will intersect and create a seamless experience for users who opt to communicate through burgeoning digital assistants or applications that rely heavily on cloud-connected data. Once again, CNTK allowed researchers to make use of sophisticated optimizations by way of learning algorithms that helped users and computers tap into quickened learning algorithms. One component of that AI strategy is conversation as a platform (CaaP); Microsoft outlined its CaaP strategy at the company's annual developer conference earlier this year."

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The choppy, cybernetic voices of digital assistants like Siri may not sound so mechanical for much longer, thanks to a significant breakthrough in using artificial intelligence to generate realistic human speech. In a new paper, scientists at Google-owned AI shop DeepMind have unveiled WaveNet, a neural network that generates audio waveforms by predicting and adapting to its own output in real-time. The result is dramatically more natural-sounding computerized speech, which the researchers say reduces the perceived gap between human and computer voices speaking both English and Chinese by over 50 percent. The system's predictive model is a far cry from the synthesized speech systems used by "digital assistant" apps like Siri. Instead of using a "concatenative" speech system that pieces together from a library of speech fragments recorded by one speaker (in Siri's case, voice actress Susan Bennett), WaveNet is trained on a massive database, then generates raw waveforms one audio sample at time using what's known as an "autoregressive" model--meaning each individual frame of the waveform is predicted based on the frames that preceded it. The neural net was developed from a similar model called PixelCNN, which does the same for computer vision by predicting images one pixel at a time.

With new neural network architectures popping up every now and then, it's hard to keep track of them all. Knowing all the abbreviations being thrown around (DCIGN, BiLSTM, DCGAN, anyone?) can be a bit overwhelming at first. So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. Though all of these architectures are presented as novel and unique, when I drew the node structures… their underlying relations started to make more sense. One problem with drawing them as node maps: it doesn't really show how they're used. For example, variational autoencoders (VAE) may look just like autoencoders (AE), but the training process is actually quite different. The use-cases for trained networks differ even more, because VAEs are generators, where you insert noise to get a new sample. AEs, simply map whatever they get as input to the closest training sample they "remember".

Almost all the industries including manufacturing, healthcare, construction, online retail, etc. Machine learning technology is constantly evolving and the current trends in the field promise that every enterprise will be data driven and will have the capacity of using machine learning in the cloud to incorporate artificial intelligence apps. The three newest machine learning trends that will make this possible are Data Flywheels, The Algorithm Economy, and Cloud Hosted Intelligence. The coming age of artificial intelligence will include mining of medical records to provide better and faster health services.

Artificial Intelligence has effectively convinced its necessity to the entire world by performing excellently in various industries. Almost all the industries including manufacturing, healthcare, construction, online retail, etc. are adapting to the reality of IoT to leverage its advantages. Machine learning technology is constantly evolving and the current trends in the field promise that every enterprise will be data driven and will have the capacity of using machine learning in the cloud to incorporate artificial intelligence apps. Companies will be successful in analyzing large complex data and providing meticulous insights without spending a huge amount on installing and maintaining machine learning systems. The three newest machine learning trends that will make this possible are Data Flywheels, The Algorithm Economy, and Cloud Hosted Intelligence. In the coming years, every application built will be an intelligent app by incorporating open source algorithms and machine learning codes.