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Self-teaching AI will be more confident without the human data Since the invention of the first artificial intelligence, the future in this field approaches faster than we expect. Experts were predicting that the AI would beat humans in the Go game by 2027. But it happened 10 years earlier — in 2017. It took only 40 days for the algorithm AlphaGo Zero to become the best Go player in the history of mankind. It was teaching itself without the input of any human data and developed strategies impossible for human players. Next year the race for the creation of a developed, self-taught artificial intelligence will only continue. We look forward to the AI breakthrough in solving many human routines: decision-making, developing businesses and scientific models, recognition of objects, emotions, and speeches, and reinventing the customer experience. Also, we expect that AI will be able to cope with these tasks better, faster, and cheaper than people. The capability of algorithms for sel

No more specific commands: growing of NLP Use of chatbots in customer service became one of the leading trends of the outgoing year. In 2018 applications will require the ability to recognize the little nuances of our speech. The users want to get a response from their software by asking questions and giving commands in their natural language without thinking about the “right” way to ask. The development of NLP and its integration into computer programs will be one of the most exciting challenges of the 2018 year. We have high expectations about this. What seems as a simple task for a human — to understand the tone of speech, the emotional coloring, and the double meaning — can be a difficult task for a computer accustomed to understanding the language of specific commands. These complex algorithms require many steps of predictions and computations, all occurring in the cloud within a split-second. With the help of NLP, people will be able to ask more questions, receive apposite

 The debates on ethics will flare up As the AI industry makes significant progress in performing various tasks and actions in the everyday life, questions are raised regarding ethics, responsibilities, and human engagement. Who will be to blame if an artificial intelligence unit performs an illegal act? Do AI bots need any regulations? Will they be able to take over all the human jobs? The first two questions assume that one day a bot will be legally recognized as a person and could take responsibility or be punished for their actions. Although this perspective is still years away, the debates around ethics are heating up already. Considering different possibilities, scientists are trying to find a compromise regarding the bots’ rights and responsibilities. However, the possibility that robots will take all the workplaces is actually close to zero. Of course, the AI industry is developing extremely fast, but it is still pretty much in its infancy. 2018 promises to take the hyp

Deep learning will be faster and data collection better Nowadays, deep learning faces certain challenges associated with the data collection and the complexity of the computations. Innovations in hardware are now being developed to speed up the deep learning experiments, e.g. the new GPUs with a greater number of cores and a different form of architecture.  According to  Marc Edgar, a Senior Information Scientist at the GE Research, deep training will shorten the development time of software solutions from several months to several days within the next 3-5 years. This will improve the functional characteristics, increase productivity, and reduce product costs. Currently, most large firms realize the importance of data collection and its influence on the business effectiveness. In the coming year, companies will start using even more data, and the success will depend on the ability to combine the disparate data. In 2018, companies will collect customer data via CRM, ticket system

AI will refine auto constructing and tuning of models Since Google’s launch of  AutoML  last year, use of the AI tools to accelerate the process of constructing and tuning models is rapidly gaining popularity. This new approach to AI development allows automating the design of machine learning models and enables the construction of models without human input with one AI becoming the architect of another. This year, experts expect growth in popularity of the commercial AutoML packages and integration of AutoML into large machine learning platforms. After AutoML, a computer vision algorithm called NASNet was built to recognize objects in video streams in real time. The “reinforcement learning” on NASNet implemented with AutoML can train the model without humans showing better results when compared to the algorithms that require human input. These developments significantly broaden the horizons for machine learning and will completely reshape the approach to model construction