Artificial Intelligence (AI) has had a major resurgence in the past few years. If we look at the history of AI, we see a repeated cycle – it was at the forefront in the 80's too – it has been going through cycles of over-promise, investment, under delivery and investment reduction. However this time around, the technology advancements, scale and attention being paid to AI are much larger than before. The advances in big data technologies combined with cheap massively scalable infrastructure and storage is now helping us tremendously to tackle bigger and bolder problems in Artificial Intelligence. However, we are really just at the tip of the iceberg with AI.

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Robotic process automation (RPA) can be the true antidote to manual, rote work, or it can be our worst nightmare if you listen to all the drama or the hype. RPA centers on the use of artificial intelligence (AI) to apply human-like thinking to streamline a typically manually intensive process or activity; and whether we like it or not, it's here to stay. Take, for instance, the process of data extraction from documents such as invoices. Application of advanced optical character recognition (OCR) and intelligent document recognition can automate a significant amount of the job of data entry typically performed by clerks or specialized data entry staff. Interestingly, human effort is still involved with attaining the ability to hand off a process or task to a machine.

Improvements and convergences in machine learning and neurosciences combined with the availability of massive datasets and the ubiquity of high-performance scalable computing are propelling us into a new age of Artificial Intelligence (AI). The promise these developments hold is immense; so too are the risks. These challenges require both immediate and future action. Computing systems are already outperforming humans in many tasks that profoundly shape our everyday lives in the fields of transportation, communication, energy, finance, healthcare as well as defense and security. There are clear upsides and opportunities, but the unintended socio-political consequences are serious, disrupting the fabric of our social contracts, sense of human identity, dignity, and considerations of agency and personal empowerment.

This article was written by Stephanie Kim. Stephanie has a professional experience with data mining and processing including natural language processing along with a small amount of machine learning and script automation. Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let's find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets. This walkthrough will provide a basic introduction to help developers of all background and abilities get started with the NLP microservices available on Algorithmia.

There may be bad news for horror film writers - computers could soon be taking your job. A new independent horror film called'Impossible Things' has been produced in part by an artificial intelligence tool. The creators have billed the film as the'the scariest and creepiest horror film out there.' The AI software was used to develop'perfect plot twists' for the film, which is about a grieving mother who, after the death of her daughter, is driven to insanity by a supernatural being Impossible Things is a horror film which was reportedly produced by an artificial intelligence (AI) software tool. The tool analysed audience response data to help the writers craft plot points that connect with viewer demand. The AI software was used to develop'perfect plot twists' for the film, which is about a grieving mother who, after the death of her daughter, is driven to insanity by a supernatural being.

AI good for internal back office and some limited front office activities; however, still need to see more adoption of QC in the Net and infrastructure in companies to expose their services and information to the public net & infrastructure. Deep learning, as explained by tech journalist Michael Copeland on Blogs.nvidia.com, is the newest and most powerful computational development thus far. It combines all prior research in artificial intelligence (AI) and machine learning. At its most fundamental level, Copeland explains, deep learning uses algorithms to peruse massive amounts of data, and then learn from that data to make decisions or predictions. The Defense Agency Advanced Project Research (DARPA), as Wired reports, calls this method "probabilistic programming."

Brands are increasingly leveraging the power of artificial intelligence to make the consumer experience more relevant and to better-approximate human-to-human interactions. AI can allow companies to personalize the marketing experience while saving money for brands. Brands are using artificial intelligence to build better customer experiences and they're just getting started. IDC predicts that global spending on cognitive systems will reach $31.3 billion by 2019. Accenture found that AI could double economic growth rates by 2035 and boost labor productivity by 40%.

So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, provided tips on how to be successful in large scale machine learning.

"Continuous improvement is better than delayed perfection." Over the last couple of years, I have seen a lot of handwringing among the Technorati about how innovation is dead and that, from now on, there will only be boring iteration. The Mobile Revolution caused a dynamic shift in the way people interact with computers in ways that we can both measure … and have no idea it is even taking place. Smartphones gave rise to the apps economy and endless opportunity. But, as all cycles do, the luster of the Mobile Revolution is beginning to fizz out. It's incredibly difficult to build a new app and become a success, smartphones have become commodities and the cloud is so ubiquitous that people hardly give it a second thought anymore.