Despite some massive strides over the past few years where machine learning (ML) even assisted and enhanced the core search engine algorithm of Google, we have witnessed its use to just a limited variety of applications. In 2017, machine learning and AI is set to encompass more domains. With technologies such as neural networks, deep learning and natural-language processing, experts predict that more advanced systems will come into play that comprehend, learn, forecast, adapt and potentially function autonomously. As the systems learn, forecast and adapt, they can bring a change in future behavior, which in turn would pave the way for more intelligent programs and devices being created. Such a change can be attributed to the blend of advanced algorithms and programs, huge data sets that feed them, and all-embracing parallel processing power.
By leveraging AI and machine learning, the banking sector can create replica of real-time transactions as well as predictive models of operations based on how likely they are to be fraudulent. For businesses trying to get ahead in the race of digital innovation, AI and machine learning could help examine various business scenarios (including even one or two high-impact scenarios) by driving precise and specific business value.
Even consumer applications can benefit from this technology by considering prior purchase history to offer better recommended products, which would improve the app’s user experience with time.
For technology vendors, 2017 to at least 2020 will be the primary battleground for creating intelligent systems that can be made to learn, adapt and potentially act on their own (autonomously) unlike their predecessors that simply carried out predefined instructions. According to Gartner, intelligent apps will be one of the top strategic technology trends of 2017.
Intelligent apps that consist of technologies like virtual personal assistants (VPAs) can make handling mundane, daily tasks (like prioritizing emails, highlighting interactions and important content etc) faster, easier and efficient. But if you think that intelligent apps just mean new digital assistants, think again. AI-enabled capabilities will be infused in almost all software categories that exist, from enterprise applications to security tooling and more. With the use of AI, the focus of technology providers will be on three key domains namely advanced analytics; interactive, immersive and continuous interfaces powered by AI; and AI-powered business processes that are increasingly becoming autonomous. According to Gartner, a majority of the globe’s largest 200 companies will take advantage of intelligent apps and utilize the complete toolkit of analytics tools and big data by 2018 to improve their offerings and give a boost to their customer experience.
So, let’s wait to see how the intelligent apps evolve and become popular with time.
Automated customer service has been around for long but this year, automation is going to touch new heights by extending its ambit to newer fields. So, apart from making automated customer service better, AI and chatbots – with their better understanding of context, will automate even online customer service.
More and more jobs will benefit from automation in 2017. With the use of AI in smart devices, your devices would learn about your preferences and patterns of use, thus making better and more accurate recommendations and suggestions. For professionals, say journalists, this could be a big game changer as they can multi-task easily and at a rapid pace.
Automated driving with driverless cars is also expected to emerge as a big thing this year as is industrial robotics. Though tests are still carried out under simulated environments, it will be interesting to see how effectively they can be deployed in real life.
GANs (Generative Adversarial Networks) are the other buzzword this year. Invented by OpenAI research scientist – Ian Goodfellow, GANs refer to systems consisting of one network that learns from a training set and subsequently generates new data, while another attempts to distinguish between fake and real data. These networks, when working in collaboration, can produce extremely sensible synthetic data. By using this approach, video-game scenery can be generated, pixelated video footages can be de-blurred, or stylistic changes can be made to computer-generated designs. But above all, what makes this approach interesting is that it gives computers a powerful mode to learn from unlabeled data, which many believe is the key to augmenting the intelligence of computers in the forthcoming years.
Some innovative and revolutionary ideas have changed the technology landscape over the last few years. From leveraging nanotechnology in the healthcare, agricultural and IT sector, to creating energy-efficient green systems driven by alternative fuel, combating diseases with biotechnology and genetic engineering, or bettering lives with new materials technology, tech innovation is noticed almost everywhere. From mobile internet and cloud technologies to autonomous vehicles, advanced robotics and 3D printing, tech innovation is changing the world we live in faster for the better.
If you want to assess how far we have come since the industrial revolution of the 19th century, just consider a simple example. The fastest supercomputer in 1975 costed $5 million while an iPhone 4 with similar performance was available just for $400 in 2013. The digital revolution that started in 1950 with the creation of first electronic computers was soon followed by information revolution, both of which were driven by the innovative idea of connecting people and making sharing of information fast and easy. When you consider the history of the innovation, you will find that every major change induced significant economic and social changes too. Thus, we can say that the history of tech innovation has shaped our today and made us dream for a better tomorrow.