Artificial intelligence (AI) assistants are the go-to things today. People from all age groups are taking to AI assistants in a big way to get things done. The more we use AI assistants, the better they are getting at their job.
IDC says that the amount of revenue that companies would make from AI systems will grow from about $8 Billion in 2016 and reach the $47-billion mark in just four years (by 2020). By 2018, we will see almost 50% of all the consumers interact with a cognitive computing system frequently. Reinforcing this is a McKinsey study which shows how tech giants, including Baidu and Google, have spent more than $20 billion in 2016 on doing R&D on artificial intelligence (AI) systems or buying companies involved in this space. It’s no wonder that Google’s search has evolved tremendously over the years to give sharp, accurate results, and within the given contexts. The investment which companies like Google are making in AI systems is paying off, and we’re seeing the difference as we engage more with AI systems.
This makes AI systems a thing you cannot ignore. If you are in the business of technology usage, you need to consider AI systems seriously, and apply it in the way that is most appropriate.
The Hype Cycle for Emerging Technologies 2017 organized by Gartner threw interesting insights our way about the impact of Cognitive Computing and Deep Learning. It is evident that these two technologies will have a big impact on businesses across industry verticals and across the globe in the next two to five years. We are also quite certain to have Smart Robots make an impact on business in our lifetimes.
With the maturing of AI systems, we need to be proactive about devising and rolling out the necessary AI regulations in time. Because, and in the words of Elon Musk, “if we’re reactive in AI regulation, it’s too late.” Without regulations to govern AI systems, we are at risk. Take the case of autonomous cars. Who is the driver, and who will be legally responsible for consequences? Existing laws and policies will need to evolve and adapt to emerging AI and related technologies.
Here are some real AI and ML (Machine Learning) industrial use cases:
Data security – A large part of ensuring data security is about finding patterns and being able to predict what is going to happen and respond to breaches. When it comes to personal security, we find ourselves quite often in public environments under the scrutiny of security cameras such as at airports, banks, malls, or movie theatres. So, vision and face recognition is going to the next level where it can detect and predict the faces. Facial recognition is poised for rapid adoption. Smart technologies deployed in Apple or Samsung phones are going to take care of it. That will help us in having a safer society.
Financial trading – Look at how high-speed trading algorithms are able to make split-second decisions on stock and trading to generate profits at a speed and frequency that is impossible for a human trader.
Healthcare IBM Watson heralds in the era of cognitive healthcare. Here is a system that uses data to build knowledge and make decisions. In a room of 10 doctors, you can consider Watson as the 10th doctor with a computation capacity and accuracy levels that humans cannot match. This is a very good development for humanity, especially when we think of the outcomes Watson claims it can potentially generate in the future: making an accurate diagnosis, keeping doctors better informed, recommending better treatments, and prescribing better medication. Healthcare is one big area where AI can play a big role.
Marketing personalization – You visit a particular website to check what shape of frames suit your face but do not make a purchase. Next thing you know is that you’re being bombarded both in the real world and over multiple online channels – which increases the probability of you actually buying that specific brand and shape of frames. Personalized marketing is rampant, but it’s also refining the user experience through subtle yet persistent new methods.
Fraud detection – Every bank today has a risk and fraud engine. This will evolve and mature further with deep learning.
Recommendations – Intelligent machine learning algorithms analyze your activity (for example, on Amazon or Netflix) and compare it to the millions of other users to determine what you might like to buy or binge watch next.
We are now familiar with technologies ranging from Natural Language Processing to smart cars. Language processing is where machine learning algorithms with natural language processing can stand in for customer service agents and more quickly route customers to the information they need. It’s being used to simplify legal jargon mentioned in the contracts into layman language. It is also helping lawyers sift through huge volumes of information in order to smartly prepare for a case. Smart cars not only integrate with IoT but also learn about its owner and its environment.
So, is this all for real? To know the answer, look at the 2017 list of Global Top 10 publically traded companies having the highest market capitalization. There are no oil and gas companies today in the Top Five. The Top Four companies in the Top Five use AI, ML, and data as sources of differentiation.
All of these technology firms are competing today on data, and the analytics that comes out of the data. Why? Because as the saying goes, data is the new oil, and this has been proven true.
At the end of 2007, half of the companies in the global list of Top 10 firms by market capitalization were oil and gas companies. In the Top Five, we had two oil and gas companies. Also, other than the Microsoft, none of the others (i.e., Alphabet, Amazon, or Apple) were in the Top 10.
Today, we don’t see oil and gas companies on the list. They have been replaced by technology firms handling tremendous quantities of data. This new breed of top firms is making correlations and using data to add value to their businesses. These are firms that are investing significantly in patents, technologies, and skill sets (read data scientists) related to AI. These are also the firms making the most progress, having real business results and real-world impact as well. Look at AI in the automotive world today. The world today is very aware of autonomous cars. Autonomous cars are changing the landscape of the automotive industry. While traditional players form a part of the automotive industry, who had expected Google to emerge as a major player in this space? With so much happening in this space, including renewed focus on battery technology and green initiatives, even players like Uber and Lyft are seen investing in auto companies. PE firms and their investments too are also a good indicator of those technologies and areas that are set to rule the world.
The auto industry will see a big change not only in the way machines are made, but also in the way regulations evolve and are formulated. Similar to how AI is changing the landscape of the automotive industry, AI applications will also come to play a larger role and have a bigger impact in other industries as well, such as:
- Aerospace Oil & Gas (where AI helps find the right place to drill a well)
- Telecom (where AI technology is used via cognitive chatbots to provide superior support services to customers)
- Gaming (where machine learning is used expertly to reduce manual testing of the software platform)
While AI and related technologies deliver clear and substantial benefits to businesses, it will have an impact on jobs. With intelligent systems increasingly supporting decision-making, there will be fewer jobs. But this must be looked at positively. In addition to business and real-world impact, people will also upskill to acquire newer, more relevant skill sets to fit into newer job roles. People will now have to learn how to work along with the machines and along with the intelligence that is coming from the machines. Added to inputs from AI technology, we have to use our emotional intelligence to make decisions.
AI and related technologies will play a big part in helping address, tackle, and resolve major problems that the world faces today such as climate change, food- security, healthcare, protecting endangered species, and energy management. It is even transforming how we learn! These are some of the future areas where AI technology will start to impact. It is also in these areas that we will see a lot of cognitive systems evolve and create real benefits.