For a long time, artificial intelligence was one of those technologies that sparkle the imagination of every person. Also, it was the favorite theme in science-fiction, but now a lot has changed. Today AI excites public and gains traction in everyday practical scenarios.
In recent years we saw a huge rise of AI in different industries around the world. Businesses use artificial intelligence development to improve operations, boost customer experience and generate innovations.
With such great popularity in different areas, we expect the rapid growth of AI in 2018. Let’s together take a look at what artificial intelligence future is going to be.
Here are top 5 artificial intelligence trends in 2018:
Data is like the fuel for artificial intelligence. As data collection, storage and analysis abilities considerably improved over recent years, most businesses found themselves with a big potential resource, but no idea of what to do with such high volumes of information.
Recently, that started to change, as people skills started to catch up with the technology. But there remains a lot of difficulty around how data is handled and used. Many businesses are starting to have a deeper understanding of the particular skills that you need to have benefit from existing data and how they can get those skills from training up their people, or recruiting someone new.
Businesses are only trying to understand how AI developers and data scientists work differently from traditional application developers, what tools they use, and how to add up them into app development teams cohesively.
2018 is the year of companies’ progress, as they need continue to broaden their skill set, bringing new data scientists on board and training existing developers. That will be a huge field for bringing innovation.
In the last twelve months, chatbots have increased in popularity. Regardless if it is healthcare patient, retail banking customer, retail shopper, or whoever else, businesses’ want to provide users with the same experience online as they would have in-store.
Chatbots are really very great for that because they can respond to customers very quickly and deliver individual care by taking advantage of algorithms and data analysis to determine the category a client falls into. You totally need to look out for more of this in this year.
Machine learning frameworks
One of the biggest surprises of the previous year was a strengthening of machine learning (ML) frameworks. There have been ML and AI projects and libraries for a long time, but recently we’ve seen a lot more investment in building artificial neural networks and deep learning libraries. Some of the big enterprises have poured huge amounts of efforts and resource into this, pulling in scarce talent. Also, with their possibilities, they have transitioned many researchers from the academic to the corporate space.
In 2017 we saw quite a few companies that have started to open up their frameworks with recent collaborations like Gluon from Microsoft and AWS, Onyx from Microsoft and Facebook. They are collaborating because there is an obvious benefit from building together, such as more freedom of choice for developers and faster innovation. Also, there is a big push in the direction of the open source and cloud computing in the tech industry today.
Most of us use a smartphone, if not multiple smartphones every day. Probably more than half of your mobile apps will have AI functionality, either supporting it in the back-end or directly embedded in it. For instance, your keyboard learns how you interact with it to get better.
If you visit any online retailer website and want to buy something, you will get a lot of recommendations based on your typical buying habits as well as purchase history using an AI engine. Also, navigation apps and ride-sharing apps use AI to make possible connecting various users on a route. We are sure that smart apps are likely to continue to gain popularity in 2018.
Any new technology may have some disadvantages. The issue of AI is how humans can pass on their own prejudices and biases to algorithms with damaging results, in anything from language translation to crime prediction. Vendors have to consider offering tutorials and tools to help less experienced developers, data scientists and businesses to gain an understanding of data itself and the human impact of artificial intelligence.
This should get a lot more attention, and we hope to see a real action to address this challenge in 2018. Together, we can create a more structural approach to the problem.
Top 10 AI Technologies:
Nowadays, the market for AI technologies is flourishing. Beyond the mainstream and the big media attention, the internet giants and many startups racing to acquire them, there is a large increase in adoption and investment by enterprises. AI today includes a range of tools and technologies, some time-tested, others comparatively new. We want to share our top10 hottest AI technologies:
Natural Language Generation
It is making text from computer information. Currently used in report generation, customer service and summarizing business intelligence insights.
That is the current boom of the media, from simple chatbots to complex systems that can communicate with humans. Currently used in support, customer service and as a manager for smart homes.
Sence and transform human speech into a format useful for computer apps. Currently used in mobile apps and interactive voice response systems.
Provide APIs, algorithms, training and development toolkits, data, as well as computing power to train, design and deploy models into processes, apps, and other. Currently used in various enterprise apps, mostly `involving classification or prediction.
Automated Decision Management
Engines that put in logic and rules into artificial intelligence systems and used for early setup/training, tuning and ongoing maintenance. It is a full-grown technology that is used in a wide range of enterprise apps, performing or assisting in automated decision-making.
Apps and graphics processing units specifically created to run AI-oriented jobs competently. Currently mainly making a difference in deep learning apps.
NLP & Text Analytics
Natural language processing (NLP) supports and uses text analytics by making possible the understanding of the meaning and sentence structure, intent and sentiment through statistical and ML methods. Currently, it is widely used in security and fraud detection and apps for mining unstructured data. Also, it is used in a wide range of automated assistants.
Deep Learning Platforms
That is a special type of ML consisting of artificial neural networks with multiple layers. Currently mainly used in classification and pattern recognition apps powered by very large data sets.
Robotic Process Automation
It uses scripts and other methods to automate human action to maintain well-organized business processes. Currently, it is used for performing tasks where it’s inefficient or too expensive for humans to execute a process or a task.
Facilitate more natural interactions between machines and humans, including but not limited to touch and image recognition, body language, and speech. Currently is mainly used in market research.
But here are some problems that AI may face in 2018:
We’ll start to see trickle-down effects regarding AI as we move forward in the security domain. If you consider the hierarchy of those working in artificial intelligence, you’ve got DeepMind at the pinnacle, along with Google and other key companies.
They are all doing some seriously expert, exciting intelligence work that is moving the boundaries of what is possible in these systems. As they continue to innovate, the security industry will start using their more highly developed systems and techniques to the benefit of our own products.
AI Skills gap
It seems that in the future AI will also be used particularly to try and combat the gap of skills in cybersecurity. It’s well understood that there basically aren’t enough people with the necessary skills within the industry, so it’s up to vendors to bring in these services. They also can make their products as easy to use as possible, to reduce the level of technical skill needed to run them. There will be a real move towards making AI as useful and simple as possible for teams to use while the industry looks for ways to deal with the skill gap.
The wrong use of AI bots
The use of AI bots for placing more targeted phishing emails and adverts is already here. That is possible because of analyzing of large amounts of social media information of people that they target. But that can be a huge vulnerability.
These chatbots are very popular in customer service. Therefore companies position them as a system that people can trust. Attackers will look for the ways to use this trust and create chatbots for obtaining bank details from people or anything else.
Once enterprises and companies overcome these obstacles, they will be able to benefit from AI transformation in customer-facing apps and developing a consistent web of enterprise intelligence.