What to Expect from the World of Artificial Intelligence and Machine Learning in 2019?

Approaching the end of current fiscal year, I take a serene glance at the memorable moments and guess what? I am quick to realize there was hardly any conference, webinar, debate, event or discussion in which there was no mention about Artificial Intelligence and its subset technologies, specifically Machine Learning, Deep Learning, Reinforcement Learning and Neural Networks. In fact, the latest 3-days conference I attended was titled ‘When Human Becomes Digital’ and was centred around artificial intelligence.

Analysts, as well as technologists, believe AI has and will continue to reign the world of technology for at least the next 2-3 years. The Ripple Effect created by these emerging technologies will gradually perish. Eventually, AI will become more consistent and stable as witnessed with other life-changing creations such as electricity, automobile, internet, mobile and smartphones.

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For the year ahead and probably longer, expect astounding groundbreaking excitement to continue with AI. It is because AI is still in its evolving phase. The promises it makes in changing life, society and business status quo, go far beyond anything that could be thought over during previous major revolutions. In fact, Artificial Intelligence visualizes a future where machines do all of the operational work as well as participate in the intellectual stuff that requires thinking.

No clear picture on whether AI will free human resources from operational workload to pursue deeper meaningful activities, or displace jobs resulting in global unemployment, open up new opportunities for the technically oriented minds to build and deploy new skills or create disruptions which may cause more unrest rather than benefit mankind. It’s too early to draw conclusions, but yes, it’s a topic that will continue to be the focal point of technical debates and discussions.

AI taking Centre Stage in International Affairs
Major global powers in 2018 are seen stepping up security measures to safeguard their national interests when it comes to international trade, economy, defence and foreign affairs. This has been more than evident in the straining relationship between the world’s two AI super nations, the United States and China.
While the US Government has imposed tariffs and export restrictions on goods and services used to create AI, the Chinese Government has hiked up its efforts to become self-dependent when it comes to AI research and development. Chinese smartphone manufacturer Huawei announced plans to build its own AI processing chips, squashing the need for the country’s flourishing AI industry to rely on US chip makers like Intel and Nvidia.
Not to forget the public criticism that came Google’s way for its apparent interest in doing business with Chinese tech firms, many with connections to the Chinese Government, while moving out from arrangements to collaboratively work with US Government agencies following concerns the leader’s technological acumen may get militarised.
With loyalist philosophy enjoying a renaissance, two apprehensions seem to be apparently imminent here. First one is that AI could be adopted by dictatorial administrations to enforce restrictions, such as the rights to privacy or freedom of speech. The second danger is that these intellectual tussles between nations could compromise the spirit of coordination and cooperation across the global academic as well as industrial organizations.
This framework of open collaboration has been highly influential in the rapid development and deployment of nascent technologies like artificial intelligence and machine learning, which we see taking place today. However, constructing walls around a nation’s AI development is likely to slow down that pace of progress. In particular, restrictions and measures taken by different nations may adversely impact the development of common standards around AI and data, which could greatly enhance the usefulness of the powerful technology.

Inducing Transparency in AI
Though AI as a technology has seen exponential growth in a short span, its adoption across broader platforms has been hampered by the popular black box problem - the regulatory compliance and implementation issues arising out of the hard-to-explain phenomenon of sophisticated AI. As you already know, AI produces an appropriate result when fed with sufficient data. However, the matter gets really complicated when the algorithms deal with human data for domains such as healthcare, life sciences, banking, accounting, etc.
The adoption of AI across wider society – particularly when it involves dealing with human data – is hindered by the well known "black box problem". Mostly, its workings seem quite clandestine and indecipherable without a profound understanding of what the algorithms are actually doing.
For humans to realize the full potential of Artificial Intelligence, they need to know what it is doing with their data - why and how AI makes its decisions when it comes to issues that affect human lives - so that they can trust an Artificial Intelligence system. What makes AI immensely useful is its fantastic ability to connect and draw inferences which may not be evident or may even seem counterintuitive to us, but this fact is often tough to articulate.

It’s not just public reassurance that will help to gain trust in AI and ML systems. In fact, the transparency in data, as well as algorithms, is essential to expose any bias that will, in turn, benefit both research and business. Analysis unravels the fear of organizations that they may face liabilities in future in case the current technology is later found to be unethical or even unfair. So the year 2019 is likely to witness technology leaders working towards inducing transparency right in the designing phase of AI and ML systems.

Recently IBM unleashed mechanism built to enhance the traceability of decisions into its Artificial Intelligence OpenScale technology. This powerful concept gives real-time insights into not only the decisions which are being made, but how those are being made, deducing connections between data that is used, measuring decision as well as potential for bias, if any, in information.
The General Data Protection Regulation popularised as GDPR, that came into effect across Europe this year, protects citizens against decisions which have either legal or other significant impacts on their lives, decisions made solely by machines. While this bubble hasn’t gathered enough heat to burst yet, its arguably an intriguingly attractive topic of public discussion that is anticipated to grow during 2019, further inspiring enterprises to put efforts towards inculcating transparency in the technology.   

Deeper Penetration of AI into Business Ecosystem

This year we have seen enterprises waking up to AI calls and getting a fair understanding of what the technology is actually capable of doing. The business data assimilating over years are now being considered by organizations as the most precious possession which can be used to their biggest advantage through the deployment of appropriate artificial intelligence systems.

In many financial services consulting firms minibots, automation, machine learning and adaptive intelligence are becoming an integral part of the team at a fast pace because machines are being accepted as complementing human brain power. In one of my previous articles, I have written about artificial intelligence and machine learning systems augmenting bank fraud detection.

As machine learning (ML) and artificial intelligence (AI) applications continue to routinely parse massive real-time logs of millions of transactions every few seconds, the human resources have promising opportunities too to focus on areas that need critical thinking and natural human intelligence. Manufacturing units use predictive technology to precisely learn what workloads machineries can be put through and under which circumstances they may break down or fail.

Next year we should expect the confidence in smartly evolving, predictive technology to grow further, greatly supported by skills and learnings these AI and ML systems have developed over time.
Leading organizations are increasingly automating HR practices. Artificial Intelligence is being increasingly implemented in resume scanning and video interviewing. AI will branch out into support functions such as Talent Acquisition or optimization of supply chains, where decisions around hiring as well as logistics, will become increasingly informed via automation. AI applications for managing compliance and legal issues are also likely to be adopted on a larger scale.

As the new AI tools will continue to be adopted and deployed, many organizations will offer the artificial intelligence and machine learning based applications as services, thereby enabling smaller businesses a share of the AI space, too. More enterprises are expected to channelize new revenue streams through proper utilization of their own business data. Some of the business enterprises who have built up big transactional databases with massive records of customer behavior within own domain are beginning to transform their practices into DaaS (data as a service). Companies like Cartesian Consulting, specializes in analytics that helps businesses improve customer value, marketing spends, and business decisions. The Solutions division at Cartesian blends in AI and ML models into ready-to-use products. In 2019 more business enterprises will adopt this strategy as they come to understand the value of the information they own.

AI to Create More Job Opportunities
As I mentioned earlier in this post, AI is still evolving and hence it's not an easy task to predict the final shape it will take and by when. Looking at the potential of AI, it will definitely displace existing human jobs which are operational and repetitive in nature. However, Gartner predicts that by the end of next year, AI will be creating more job opportunities. While 1.8 million jobs will be lost to automation, with manufacturing singled out in specific as most likely to take a hit, 2.3 million new jobs forecasted to be created. The impact of AI is expected to be more profound in particular on manufacturing, education, healthcare, banking, customer services and the public sector.

An important factor influencing large-scale rolling out of AI in an augmenting capacity when it comes to implementing it in non-manual jobs is the need for the cognitive ability which none other than human intellect can offer. Employees working in warehouses, customer care, call centre and retail have often been replaced in a massive way through automated technology. But when it comes to professionals like doctors, chartered accountants and lawyers, AI service providers have made concerted effort to present their technology as something which can work in parallel with human experts, assisting them with repetitive tasks while leaving the final decision to them.
This implies those industries benefit from the growth in human jobs on the technical side, those needed to deploy the technology and train the workforce on using it, while retaining the professionals who carry out the actual work that needs intellect and cognitive capability. In the financial and banking sector, back-office functions are increasingly being handled by machines, which could result in the displacement of existing human resources.

Rise of AI Assistants
Artificial Intelligence, Machine Intelligence or Adaptive Intelligence, no matter what you prefer to call this wonder technology, it is becoming part and parcel of our daily life, to the point that most users don't give a second thought to the fact that AI-driven predictions are at work, when they search Google, shop at Amazon, book hotels at goibibo, or watch Netflix, to give the seamless experience. A slightly more evident sense of engagement with robotic intelligence comes into play when we interact with AI assistants – Alexa, Siri, or Google Assistant, for example – to help us decipher and make sense of the myriad of data sources available to us.
In 2019, more users will use an AI assistant to arrange calendars, plan trips and order food or even OTS drugs. The AI services will become increasingly useful as the AI and ML apps learn to expect our behaviors better and comprehend human habits.
Data gathered from users allows application designers to understand exactly which features are providing value, and which are used below a certain threshold, perhaps consuming valuable resources through bandwidth or reporting which could be better utilized elsewhere. Consequently, functions which we do want to use AI for, such as hiring cabs and ordering food, or choosing restaurants to visit – are becoming extremely streamlined and accessible.
AI assistants are designed to become increasingly efficient at understanding the behavior of human users, as the natural language algorithms used to encode speech into computer decipherable data, and vice versa is exposed to huge amount of information about how we interact.

So as we step into 2019, a whole year of enthusiasm and excitement-filled journey into the world of Artificial Intelligence awaits us.

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