AI, MI, ML, Big Data to Hit New Levels of Maturity in 2018
The era of hardware becoming intelligent is reaching new heights, as evidenced by artificial intelligence (AI) in the mainstream – to wit, Alexa, Siri, Google Home and so many more smart applications. AI, machine intelligence (MI) and machine learning (ML) will combine to jump an order or two of magnitude in maturity throughout 2018. Any one, two, or all three technologies, when coupled with Big Data, will begin to reach advanced levels of sophistication with some real-world applications emerging. That, it is hoped, should interest businesses because this evolution has the potential to see return on investment start to develop.
Combinations of AI, MI and ML Increase Usefulness of Big Data
Increased maturity in the AI, MI and ML platforms will reap rewards because of the collaborative environment they can create, together. How? By evolving data management capabilities, especially analytics, and the ability to make meaningful information out of mountains of data. That is the turning point – and the challenge of Big Data and its integration with intelligence. Some think 2018 will be the year that real Big Data analytic algorithms and applications will emerge and bump up the capabilities of data analysis to the next level. If so, expect a new generation of intelligence to show up in a number of places.
This is significant because, thus far, the lack of potential to monetize these technologies has the business world hesitating to do much investment. However, emerging apps and advancements are being promised for 2018. It is expected that such apps will bring new impetus and incentives to begin integrating them into business applications and stimulate investment.
Such will also be the case for MI, which will benefit from advances in data analytics, AI, and ML. MI will start to see a deeper integration into these same business metrics.
Some numbers point to optimism. In the information technology, telecommunications and consumer technology markets, AI investment will reach $47 billion by the end of 2019, according to forecasts by IDC. Much of the spending is expected to drive changes for better customer experiences. Examples of that are discussed later in this piece.
Deep Learning to Take AI to Next Level
What will be the biggest bump for AI is something called “deep learning.” AI will go from the typical if X = Y, then C, else D, of typical smart devices to artificially intelligent systems (AI 2.0) that will actually learn, suggest and automate processes by analyzing patterns and behaviors. Deep learning will become an integral element of AI and the major factor that will take AI to the next level.
Perhaps the latest example of that is an AI-based computer system, called Libratus. It has mastered a game once considered too difficult for AI. This poker bot has played thousands of games of heads-up, no-limit Texas hold’em against a cadre of top professional players at Rivers Casino in Pittsburgh. Moreover, it beat them all!
Why this is a big deal is because poker isn’t just analytics – it requires both reasoning and intelligence, even emotions, of the type that has proven difficult to impossible for machines to imitate. Because an opponent’s hand remains hidden, it is extremely complicated to use logic in developing the perfect strategy, regardless of the complexity of the algorithm, or depth of the database, of several possible approaches your opponent can take. And, this is a classic example of how nonlinear elements such as body language and human-type actions/reactions are part of the advanced AI equation.
The “gotcha” here is that in no-limit Texas hold’em, there is no single “correct” play, as in chess, for example. Therefore, it is virtually impossible to run all possibilities, regardless of resources. Instead, the AI must use “game theory” to calculate optimal plays presented by the uncertainties, weight them and pick the most likely opponent scenario.
While this is only a game, the implications are huge. It shows that AI does have the potential to “think” and use logic in the fuzzy realm.
AI to Play Bigger Role in Automated Systems
In 2018, we will also see AI play a much more significant role in automated systems. In the wireless world, it will enable autonomous self-configuring networks and will run virtualized networks. This will be a must for 5G where it will be impossible to manually manage the vast network ecosystem.
The next year will also see AI move more into the “thinking” realm, meaning it will start to take over many of the routine and mundane tasks that lead to a productive, rather than simply a deductive, solution. For example, it can take over many of the tasks in test and measurement that people once did, and provide solutions based on intelligent analytical assessments. It can also be used to monitor and analyze data in field trials and lab scenarios and to offer solutions to the decision makers.
There is a wide range of opinions around how fast, where and how pervasive AI, and its support platforms, will permeate the human ecosystem. However, the next generation of AI, MI and ML are emerging and, coupled with big data, have the potential to disrupt, decisively, the status quo. 2018 will give us a glimpse into that realm, as well.
Designing Ethics, Security into Intelligent Robots
While there is a lot of discussion across a number of vectors, to discuss all, or even most of them would be interminably lengthy and many of the same issues stratify across all the sectors. So as an example, let us take the particular issues of ethics and security. The following is a rather interesting position that bring this close to home.
It was penned by Guido Noto La Diega a Northumbria University School of Law, faculty member. It is titled “The European strategy on robotics and artificial intelligence: too much ethics, too little security.” It looks at the increasing interest in the ethical design of intelligent robots, which integrate AI, ML and MI, including some recent reports and the European Parliament’s resolution on civil law rules on robotics. The EU Parliament is the first legal institution in the world to have initiated work of a law on robots and artificial intelligence. The report revolves around the question of ethics in the AI realm, and what the options are for setting boundaries for AI.
Look for Increased Penetration into Customer Service
While AI and its cohorts have many useful applications across a wide swath of platforms, eventually the issue of these components in the human realm will have to be addressed. Expect 2018 to ramp up the debate around this vector.
However, circling back to areas where AI, MI and ML will find significant integration, one is the customer service sector. Because it will be a while before the actual customer service agents are artificial, 2018 will see the two prime customer service metrics – urgency and emotion –augmented by the next-generation of AI. Algorithms will aid customer service agents in assessing customer dispositions by what they say, even how they say it. These algorithms will aid and direct the agents in their responses and actions.
Another similar customer service application, according to Computerworld, is in the hospitality segment. Hotel groups like Marriott, Sheraton and Westin are interested in deploying virtual assistants in room services. Sheraton Grand Hangzhou Wetland Park Resort and Westin Sanya Haitang Bay Resort stated they plan to install Alibaba’s AI-powered voice assistant Tmall Genie in their guest rooms to control curtains, lamps, TV, air conditioning, call room services and provide travel suggestions
Extrapolating, it is quite possible that AI, along with deep learning and MI can, eventually, master the assessment of metrics, such as urgency and emotion, and become a stand-alone response system in many of the cases. But that is still some distance away.
For now, augmentation and limited functions seems to be the immediate direction. According to a recent Gartner report, it is believed that, by the year 2020, 85 percent of customer relationships, of one sort or another, will be through AI-powered services. Exactly what that will look like is still a bit fuzzy.
Another cutting-edge application is with Lyft. Lyft uses AI-empowered self-service to eliminate the human factor (except for the driver, but Uber is looking to do that as well). The customer manages all the variables – time, place, payment, etc. Because there is very little wiggle room in the process, it is reliable, repeatable and consistent, i.e. low stress. 2018 promises to unveil more of these types of approaches to customer service in scenarios that can benefit from such autonomous processes.
A Chatbot in Every Pot; What Could Go Wrong?
There are a number of spoilers for this arena. Mainly because it is so ubiquitous across, virtually, every platform and technology.
Fear, uncertainty, doubt, known as “FUD” is the one big spoiler with tons of mini-spoilers underneath that umbrella. Most of the concerns around AI vector back to this in one form or another and the fantasy that machines can take over the world. OK, so that is a bit melodramatic but I am willing to go on record as saying that has crossed nearly everyone’s mind at some point or another.
Another major concern and one of the biggest challenges is in the security sector – how to keep it away from the dark side. A huge impediment to progress is that the dark side can use AI for nefarious actions, equal to what AI can do for good. It is just as capable of hacking and exploiting as defending.
There is little doubt that AI will be prolific in the dark domain, so the emphasis is on countermeasures – not much different from current philosophies. But look for an increased level of activity in 2018 as the industry attempts to understand the negative aspects of AI in relationship to security.
There is also a lot of FUD around machines taking over jobs and displacing workers. There is little doubt that some of that will occur. In fact, it has been occurring for years already. But most of the displacement has been in simple repetitive, mundane, and automated tasks. This next generation has the potential to replace thinking, reasoning and deductive activities; chatbots are an early example of that.
Ultimately, experts say it would be impossible to develop a machine that can mimic everything about a human being. There are just too many variables, including vocal imperfections and intonation, and what is often called the “gut” feeling, that are too complex for even the most advanced AI to analyze. That will not even be a blip on the radar screen for the next few years, but I am not betting against nearly perfect AI bots one day.
Finally, there is trust and the human factor. That is the final frontier. If humans, ultimately, refuse to accept and trust interacting with AI, well, that is one variable that cannot be controlled (without LSD, at least). There is a lot of interesting discussion around this particular metric but it is not one that fits into the 2018 discussion quite yet.
In the end, the areas of AI, ML, MI, and big data are just on the cusp of their next major advancements. It is looking like 2018 will be a jump starter for this.