In the summer of 2011, I was giving a lecture on machine translation at a small college in Kathmandu. That afternoon, one student asked me a question that was so complex and required such fundamental understanding of computer science that it made me realize how some students in the most remote corner of the world — with hardly any access to advanced technology and academic privileges — have the level of talent and curiosity as some of my students at Columbia University. Two years later, I hired the student to work for me at Fusemachines, where he currently leads a team of 90 engineers.

Chinese tech giant Tencent, in a study compiled by its research institute, estimates there are around 300,000 AI professionals in the world — but millions more are needed. As artificial intelligence permeates through every field in every industry, there is a war for AI talent. In fact, Silicon Valley giants are fighting and paying an exorbitant amount of money to lure the best AI engineers to work for them. “Salaries are spiraling so fast that some joke the tech industry needs a National Football League-style salary cap on AI specialists,” Cade Metz wrote in a New York Times article.

But instead of upping the salaries to millions of dollars and fighting for the same small pool of talent, we should be training engineers in artificial intelligence around the world. Young students and engineers in remote developing countries also have the ability to perform — and, at times, outperform — the ones who have degrees from elite institutions in the West. There is untapped talent in these places, and we are neglecting it to our detriment.

Educating engineers from across the globe in machine learning, deep learning and natural language processing — the most common sub-disciplines within AI — will help increase access to AI talent. Someone who experiences complex problems in his/her own country could be more suited to try and solve those problems with AI. For example, a Nepali engineer who wants to use machine learning to predict crop yields of their community will be better informed about Nepal’s farmlands than a graduate from Silicon Valley.

Similarly, engineers working for companies like Zipline and Fuse machine in Nepal and Rwanda are able to build and adapt autonomous drones to deliver medicine in remote villages in countries with poor road infrastructure. This would not only save lives but also remarkably change the livelihood of villagers who would otherwise have to trek for days to get to the nearest pharmacy. This is one example of the numerous ways artificial intelligence can be used to improve health care, fight poverty and raise the standards of living in developing countries. For that, it’s important that we invest in educating and enabling talented young engineers in such countries.

But how do we train local engineers in far-flung places to build drones, robots and complex systems? The answer is in a combination of online courses and some on-site training. Two years ago, Fusemachines launched a fellowship program that allows students in Nepal to develop high-level skills in programming and solving machine learning algorithms — eventually leading to a MicroMasters in Artificial Intelligence from Columbia University. Today, the program has expanded to three additional locations: the Dominican Republic, New York City and Rwanda.

As they complete the course, enrolled students come to class once a week and discuss the homework assignments and problems. What we’ve found is that this mix of an online course with on-site guidance works very well with the students. They learn on their own time throughout the week but still feel like a part of a class when they meet with other students in a physical location. With this model of learning, we have had many engineers graduate with certificates in AI from Columbia University.

The software development company Andela is another example that this type of talent pipeline works. Andela focuses solely on training developers in Africa before pairing them with Western companies, thus helping to reduce the opportunity gap in tech jobs. Online MOOCs like Edx, Coursera and Udacity are also doing a tremendous job of making highly sought-after specialized skill sets available for the masses through online courses taught by talented professors, scientists and engineers.

The world is rapidly moving into a new era of technology where AI will transform many aspects of human life. But if we want everyone to benefit from the development of AI, and not just a few select countries, we need to make AI equally accessible around the world. This is why we need to democratize artificial intelligence.

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Society is in a new age of digital customer service where on-demand help is expected 24/7, 365 days a year. Brands are recognising that customer service is an integral part of the customer journey, and they’re upping their game to better understand the consumer’s needs.

Artificial intelligence (AI), and more specifically machine learning, is more relevant than ever with more and more businesses investing in new technologies that connect them with their customers in real-time to provide better, more efficient services.

Delivering a superior customer experience must involve a balance between the productivity and speed of tech and the empathy, emotion, and complex problem solving that humans can provide.

Automation not domination

Some people are fearful that AI will render their careers obsolete and remove them from the workforce. Jobs will change, as they often do with the emergence of disruptive technology, but disruption is not the same as replacement — they’ll start to work smarter and faster alongside automation.

It’s important that organisations don’t lose the human element in the rush to automate customer service and save money on personnel costs. Machine learning can be used to speed up the logistical processes but it doesn’t yet have the ability to understand human emotions and the vagaries of conversations with a customer. If businesses are to avoid the risk of alienating and losing customers, maintaining these human relationships will be critical.

The purpose of AI is not to remove customer service agents, but rather to enhance their interactions with customers, eliminating monotonous tasks and freeing up valuable time. Humans will remain at the core of great customer service – but only by playing to their strengths and using artificial intelligence to augment their skills.

Whilst machine learning can clearly help businesses save significant amounts of time and money, key human competencies are still needed to take full advantage of this.

In the relationship economy, personal engagement and conversations rather than transactions are critical to ensuring repeat purchases and ultimate improvements. Machine learning can lay the foundations but businesses still need the humans who can build on this.

Customer service gets smart

The overall volume of interactions is rapidly increasing, as consumers have more questions about the products and services that they are using, and expect more from the brands that they deal with.

Technological advances have encouraged the expectation of ‘always on, immediately answered’. Machine learning can be used to drive efficiency and speed up service.

A customer may be suggested an existing answer immediately or a customer can be immediately directed to the right staff member with the relevant expertise to deal with their problem.

Understanding consumer behaviour has always been central to providing a high standard of customer service. These standards are measured by how close a company can get to the consumer.

With face-to-face interactions, it’s easy to interpret the motivations of the consumer. Over the phone and with online chat, it can be more challenging. On a website, which isn’t enabled with any degree of intelligence, it can be virtually impossible.

Essentially, AI takes the emotion out of processes to put intelligence at the heart of the customer-organisation relationship, providing intuitive results quickly and conveniently.

Organisations that implement intelligent systems which use digital interactions, such as what a customer has done on a website, what they wrote in an email or social media messages, and suggest relevant responses to agents not only help to deliver a faster, more productive service, but empower with the knowledge needed to meet customer demands.

Human versus bot

Nothing annoys customers more than speaking to a customer service agent who cannot provide definitive or consistent answers to their enquiries. With the expectation that information should be easily accessed online and the consumer desire to self-serve where possible, there is a growing dislike for the need to connect with live customer support for simple matters.

As exciting as bots have become, they’re not quite ready for the spotlight when it comes to all customer interactions. However, bots bring tremendous potential to specific tasks where they are particularly well suited —the level of maturity, in terms of big data and natural language processing is becoming increasingly sophisticated and capable of automating increasingly complex interactions.

By using bots, companies not only augment the capability of their agents and reduce their workload, but also create much better user experiences, they do this by making it easier and convenient for customers to communicate with brands, streamlining interactions, and providing relevant information at the right time.

The benefit of the latest deep learning techniques — even in circumstances where customer service requests are complex — is they’re capable of learning from a ground truth set of accurate and positive human interactions and automate complex interactions where a customer needs an answer or action immediately. In these cases, a bot can provide a great self-service experience and free up human time to focus on interactions where they are necessary.

Successful automation relies on brands actively applying the appropriate level of automation versus augmentation. Thoughtful organisations aren’t using automation to eliminate staff, rather, they are using intelligent automation to improve workforce efficiency, leveraging existing data to anticipate the needs of their customers and serve up a much more personalised experience.

The future of customer service

There is no one size fits all for customer service, or any single platform that works for every customer and businesses need to ensure they are providing the kind of service that consumers want and need.

Customers shouldn’t be restricted when it comes to the service they receive. It doesn’t have to be an either-or situation with bot or human interaction. Brands will need to bring together humans and technology, with simple interactions handled automatically by bots, and machine learning supporting agents when they are having more complex conversations with consumers.

If organisations remove personal connections, at best, they risk missing critical opportunities, and at worst, damaging customer relationships.

Machine learning is here to stay and for certain interactions, customers prefer the convenience of automation for its ease and speed. Machine learning and bots will become more proficient at dealing with complex enquiries, and in some cases, pre-empting customer enquiries with proactive communication.

Today, consumers expect help in real-time and machine learning is enabling this as we become embedded in new channels of communication. Businesses that can embrace this change and tailor their customer experience with more proactive, instant, and targeted support will be rewarded with customer trust and loyalty.

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We all remember Maxim Gorky, Rabindranath Tagore, Ernest Hemingway, James Watt, Thomas Alva Edison, Leonardo da Vinci, and the Wright Brothers as some of the few people who left their mark in the world but do you also know that all of them were partially or wholly self-taught. They did not rely completely on the education that school had to offer. These kinds of people are called autodidacts. Here is a list of such people, try and see how many you already know.

It was very beautifully penned down by the American writer, humorist, entrepreneur, Mark Twain as “I have never let my schooling interfere with my education.” Here are some proven techniques and effects which can radically change the way you learn things.

1. The Pomodoro Technique:

© Fractus Learning

A technique developed by Francesco Cirillo in the late 1980’s to help you focus your attention over a short period of time. Pomodoro is an Italian word for tomato and is called so because Francesco used a tomato-shaped timer while practising this technique. Traditionally, the timer is set to 25 minutes, but you may vary it slightly according to your needs. Now, once the timer starts ticking, you are not allowed to sneak off to web surf or chat on the phone. So, the thought is, for those 25 minutes you need to focus on the process and not on the product. You will be tempted by distractions but you will be pleased with how easy it is to get your attention back.

2. The Feynman Technique:

© Ace Tutors

The Feynman technique has been named after Richard Feynman, a Nobel Prize-winning physicist. This technique demands people come up with a simple metaphor or analogy to help them grasp the essence of an idea. You may relate to it if you try to remember that you learned a concept better when you taught it to someone else. You can use the Feynman technique to understand the ideas that you do not really get or to remember those which you understand but forget on the day of the test.

Follow these steps:

Choose a topic

Try to pretend that you are teaching this idea to a new student.

When you get stuck at some point, go back to your reference material.

Simplify the language and concept and create analogies to grasp the concept better.

3. The Memory Palace Technique:

Remember Benedict Cumberbatch in ‘Sherlock Holmes’ using the mind palace technique to capture and trace a lot of information in his head? This technique basically involves calling to mind a familiar place like the layout of your workplace which you can use as a visual notepad where you can deposit concept images that you want to remember. You can use it to remember a long list. For example, sin cos and tan theta formulas can be remembered as “some people have curly brown hair through proper brushing” to remember that sin theta is equal to perpendicular upon hypotenuse and respectively for cos and tan theta. It can still be difficult to remember this but it becomes much easier if you also add the memory palace to it. Like, Some people like Rahul have curly…You get the idea.

4. The Hard-Start-Jump-To-Easy-Technique:

© SuperThinking

Students are often advised to start with an easy question so that they do not get demotivated at the starting phase of the test, but this is not the best practice. Instead, start with a difficult problem and if you get stuck, quickly switch to the easier once. Then get back to the difficult problem again. Now, this switch activates your diffuse and focused modeswhich are very important to solve the most difficult problems in your test sheet.

5. The Einstellung Effect:

© Central Coffee

It is a kind of mindset which restricts us to a repeated use of tried and true strategies to solve any problem, even though a simpler strategical solution  exists. Now, if you observe this carefully, it is a big problem. It makes you look like a zombie and you tend to develop a mechanical state of mind. You lose creativity while solving the problem with a different technique. You can still mitigate its effect by following these steps:

Remind yourself of the effect to be consciously aware.

Enter your boketto mode. Try gazing at the sky without thinking anything. It gives your mind a break.

Adopt a beginner’s mindset.

6. The Testing Effect:

© OpenPSYC

This technique helps in improving long-term memory, often with the help of retrieving information that you once learned. This improvement in knowledge because of test taking is known as the testing effect. Testing basically strengthens and stabilises the related neural patterns in your brain and you can practice it using flash cards and by getting feedback from others.

So, the next time you learn a topic and if you don’t get it at once, try remembering the main points you have learned and then get back to the revision process. The same goes when you leave a problem half solved, thinking you know the complete solution when you actually don’t. To strengthen your neural patterns you must take enough tests on a regular ibasis.

Now, according to the Law of Serendipity: Lady luck favours the one who tries. So, start practising some of these techniques to overcome many of the problems you face while learning a new subject.

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