Hot AI Technologies: Top 6 for 2017
Artificial Intelligence or AI is no longer a term from the sci-fi novels and movies. It is a profitable industry, involving hundreds of high-tech startups and billions of dollars in investments worldwide.
The International Data Corporation report forecasts the AI industry income to surge 6 times in four years: from mere $8 billion in 2016 to more than $47 in 2020. In addition, the Forrester report states that 2017 will see 300% more investments into AI industry, as compared to 2016.
What drives such insatiable hunger? Why are IT industry heavyweights like Google, Amazon, IBM, and Microsoft so eager to pour millions of dollars into startups that sometimes promise merely a blink of success?
Because we are leaving in the age of information overload. The flow of information is so dense that terabytes of it are generated worldwide every second and that 99% of it will never be read by a human being. To say even more, an average Millennial is able to concentrate on a task for no more than 15 minutes and loses focus on a text if not engaged within the first 8 seconds.
Thus said, curating the data according to a set of parameters in order to provide exactly the information the customer requests — this is the task AI should solve. This is the obvious evolution of currently popular neural networks. However, there are still some issues, as we have already explained in this article on why deep learning alone is not the true AI.
There are other obstacles to adopting AI amongst enterprise businesses:
- 39% of respondents are still not sure how their business can benefit from using the AI
- 42% do not have any AI-based business case as of yet
- 29% have to invest into building or modernizing their Big Data management first
- 33% simply lack the skills needed to implement the AI in their business routine
The company that will create a sentient AI algorithm able to communicate with any customer and provide exactly the information requested — this company will rule the IT industry world. This is why Microsoft states AI to be its top priority from now on.
There are numerous other areas of employment for an AI, like monitoring work in hazardous environments and construction, along with 20 other industries (yes, we mean IBM Watson here) like healthcare, financial services and insurance, retail and marketing. This is why knowing the current state of affairs in the AI field is essential. Below we list top 6 hot Ai technologies for 2017.
Computer vision and image recognition algorithms
From analyzing picture contents to find cats and all the way up to finding a particular person on a video through analysis of their posture, silhouette or movements, computer vision AI developers work on designing an algorithm that will be able to understand the contents of a video or a picture, or even react to a live video streaming input. Some of the most impressive applications include self-driving cars, smarter security cams, or even a laser-firing system for zapping the malaria mosquitos! This technology is of utmost importance and will definitely deliver more and more impressive results in the years to come!
Recognition and understanding of verbal and written speech
This trend has four major components: natural language processing (NLP) and generation (NLG), speech recognition and virtual interactive assistants:
- Text analysis and natural language processing are an essential part of understanding the customer’s intent, not mere search requests. This is the foundation for providing personalized ads offers, finding relevant content for search requests and can even help recruiters find relevant applicants by analyzing their CV’s on the job boards, even if there is no full skill match.
- Natural language generation involves creating readable texts from computer data. This AI application is crucial for in-depth reports, chatbots on customer self-service portals, and for delivering point-of-need smart alerts on real-time analytics of data feeds.
- From simple self-service portal chatbots and all the way up to self-learning virtual assistants like Alexa, Siri and Cortana, these AI systems are currently in the focus of attention from the media. As the Internet of Things becomes more of a reality and less of a gimmick, these systems will become more and more affordable and ubiquitous.
- Speech recognition is an important and integral part of the aforementioned virtual assistants. Voice commands are a huge field for improvement of a human-machine interaction. Just imagine freely speaking to your assistant, controlling home appliances, writing code, making notes, surfing the Youtube videos — and without being confined by the keyboard, only with your voice. This trend is really hot and will undoubtedly grow more important with time, as the IoT hits the market.
Platforms for machine learning
IT industry giants like Google, Amazon, and Microsoft offer APIs, data sets, algorithms, development and training toolkits along with the required computing power for designing, training and deploying various machine learning models. These models are currently used across a plethora of enterprise applications, delivering various types of Big Data analytics (predictive and prescriptive analytics, streaming analytics, unstructured data analytics, etc.)
These AI algorithms involve neural networks with multiple hidden abstract layers and are best used against huge data sets for classification and pattern (or anomaly) recognition. As of now, deep learning is an important part of AI development, statistical analysis and data processing, and this trend is not likely going to change.
AI is able to recognize the fingerprints, retina images, body postures, voice and speech patterns, etc. Such AI technologies are currently used in marketing and security applications, but in future, they will undoubtedly get more implementations. AI will make the comparison of facial 3D prints more accurate to drastically reduce the chance of falsifying the results of security scanning.
Expert systems for decision-making assistance
These methods include the addition of various rules and logic into AI engines. This helps build, train and maintain scalable applications, that are capable of automated decision-making for routine business processes, so the need for human intervention is minimal. One of the real-life applications of such expert systems is using them to diagnose mammary cancer. When the AI is able to evaluate the X-ray photograph and provide the result within seconds (while also self-learning along the way) this is beneficial both for the doctor, who can treat much more patients within the same time, and for the patients, who avoid the risk of false positive diagnosis, saving them nerves, time, money and health on expensive treatment.
AI industry continues to conquer the enterprise market, and these 6 hot AI technologies for 2017 we described above will become more and more popular and important. As Narrative Science report states, 62% of enterprises will use some kind of AI technologies by 2018.
Will your business be amongst them?
Feel free to browse through the latest insights and hints on the DevOps, Big Data, Machine Learning and Blockchain from IT Svit!
What if the transition to the cloud does not increase your profits?
The biggest fallacy of nowadays world is the so-called Productivity paradox, which states that the increase in investments in the IT operations does not lead to the growth of productivity.
Guide to AWS platform migration: AWS migration best practices
One of the most common tasks we perform at IT Svit is cloud migration from AWS to GCP, Azure, DigitalOcean and vice versa, or from legacy infrastructure to the cloud.
What if a company could really inspire self-development in employees?
There are tons of materials from multiple experts and gurus, who promise to teach businesses to inspire their teams, to make the employees grow and become more productive. However, their methods rarely work. Why so? We describe our vision of why conventional employee engagement practices fail, and how to really inspire the employees to self-development.