Trends in Artificial Intelligence

From a business perspective, what counts is how Artificial Intelligence can make you money. At this point in a book, I hope I’ve conveyed the general framework that AI is here to help you automate and optimize business processes with which you deal daily. It’s supposed to make you faster, more creative, and more efficient in what you do, and by that, give you some time and money back.

In this chapter, I will look at various business domains and go through how AI is already used in practice. From the most striking applications in retail to healthcare and video games, we’ll go over use cases that made headlines in recent years. Moreover, I’ll speculate about the general directions of AI applications and how your organisation can benefit from them.

Regardless of your geographical location or your budget, you’ll be able to use AI in your organisation thanks to democratisation trend within the community...

AI in retail

The sale of goods is a prototype of all human economic endeavors. AI plays already a prominent role in boosting sales of virtually any item, be it online or offline. Just think about the following use cases:

• classifying customers based on their purchase history;
• personalization of offer for each potential customer;
• analysing demographics and proposing an individual message for each customer;
• automation of sales via in-store technology;
• customer service done by chatbots;
• analysing competitors live 24/7 and reacting to changes fast;
• optimising ad spend and marketing campaigns;

to name just a few more basic ones. They are all related to smart analytics, and while they existed before the technological revolution, AI allowed retailers to boost analytics in a tremendous way. It gave companies tools to analyse and act upon insights 24/7, perform hyper-personalization, and considerably lower the cost of operating a retail business.

In...

Manufacturing

Manufacturing produces 16% of global GDP, and for that reason, AI can hugely benefit the whole economy if it can boost manufacturing. And it seems like this is the case. I will divide manufacturing into:

• predictive maintenance,
• forecasting demand,
• factories (also partially covered in chapter about robots),

and discuss each use case below. The term Industry 4.0 emerged in 2011 to address the trend of total automatization of manufacturing. As a business vertical manufacturing is very susceptible to automation - many factories have already implemented automation to a large degree. Due to the digitization of manufacturing, it’s easy to enter with AI tools and try to optimize processes. However, the most difficult challenge lies in using AI in the way which won’t make errors in scenarios where errors would cost too much (human lives).

Predictive maintenance

Maintenance is a critical area that can drive significant cost savings and...

Logistics

Machine learning algorithms can learn how to optimally allocate resources, like fleets of vehicles, to address dynamically changing demand (e.g. passenger requests) while maximising resource utilisation. Thus it’s no surprise that logistics is another domain with AI adoption in progress.

In general, AI can help logistics in:

• demand forecasting,
• assisting last-mile delivery (from chatbots to autonomous drones),
• real-time decision making,
• creating contingency plans,
• tracking movement.

Rolls-Royce27 is working with Intel to develop self-driving ships. Rolls-Royce released the Intelligence Awareness system in 2018, a system that can classify all the nearby objects under the water. It can also monitor the engine condition and recommend the best routes.

DHL28 has developed a machine learning-based tool to predict transit time delays of air freight to enable proactive mitigation. By analyzing 58 different parameters of internal data, the...

Robotics and Autonomous Vehicles

Media and science fiction movies love stories about autonomous conscious robots taking control of the world. This vision is far from reality, and in this chapter, I will cover commercial use cases of robotics.

The most vivid imagery for robots is created by Boston Dynamics. Boston Dynamics has made tremendous progress in the last ten years, from barely walking robots to parkour performing athletic robots able to walk and run on any terrain. Each year they present innovation, and then there’s a bit of public concern about the potential use of those in military missions.

However, the reality for robots is usually more boring as they are widely used for warehousing and logistics tasks.

Warehouse robots

Tractica Research30 estimates that the worldwide sales of warehousing and logistics robots will reach $22.4 billion by the end of 2021. Robots are locating, tracking, and moving inventory inside warehouses; they are conveying and sorting...

Robotic Process Automation

A report by McKinsey Global Institute called “A Future That Works: Automation, Employment, and Productivity”41 predicts that nearly half of work tasks will be performed by some form of a robot by the year 2055. AI agents will automate any kind of job that is repetitive on various levels.

I genuinely believe that. Not only repetitive tasks will be automated but also creative ones, as we can judge by recent breakthroughs in text understanding.

Robotic Process Automation (RPA) software is a fundamental piece of automation. RPA, at its core, is just an approach to automating business processes through the deployment of bots or AI. It dates back to the 90s, and back then, it was a purely software engineering task of breaking down a process into smaller pieces and connecting various APIs to replace humans. For example, having to copy a particular text from one document to another and then sending it through email. Though useful, it was primarily...

Image generation

Deepfakes are hyper-realistic AI-generated images and videos. They entered the mainstream, making real and fake media indiscernible. This shows another side of democratising AI: an easy availability for malicious use in disinformation and malware.

Media companies are the first to engage in using image generation.

At the end of December 2019, Snapchat acquired AI Factory, a Ukraine-based startup developing computer vision products, for $166M43. Snap had previously worked with AI Factory to power Cameos, a feature that enables users to insert their selfies into GIFs to create animated deepfakes. Snapchat Cameos are an alternative to Bitmoji for quickly conveying an emotion, reaction, or silly situation in Snapchat messages.

TikTok, owned by ByteDance, is working on a similar feature: it has built technology to let you insert your face into videos starring someone else.44

Samsung engineers have developed realistic talking heads that can be generated from...

Text generation and Chatbots

Text generation had experienced the most significant breakthrough in 2019 when OpenAI announced GPT-2. This Transformer-based model was able to generate coherent pieces of text on a large scale.

GPT-250 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data.

The whole 2019 was full of surprises when it comes to text generation models with Megatron from NVIDIA being 5 times larger than GPT-2 and finally Turing-NLG from Microsoft being 10 times larger than GPT-2 (released in February 2020).

We are just beginning to experience...

AI-powered education

Education is also transformed by AI. Most of the innovations so far were on the side of running massive online classes. Companies like Coursera or edX are leading the way of online education, enrolling millions of students into courses from higher education institutions.

Nevertheless, more edtech companies are investing in machine learning solutions to track the progress of students and personalise their learning experience. AI promises global access to personalised education for anyone.

Advances in speech and text understanding allow AI to answer questions from students instantly, guiding them through the process along the way.

Reports from EdTechXGlobal and Ibis capital estimated that schools spent nearly $160 billion on education technology, or edtech, in 2016, and forecast spending to grow 17 percent annually through 2020. Also, private investments in educational technology increased by 32% in the last couple of years.

Coursera’s online...

AI in Healthcare

Artificial Intelligence can already review health records and medical data with more speed and accuracy than humans. Thus AI in healthcare can significantly increase the accuracy and reduce the likelihood of human error in:

• diagnostics,
• treatment plans,
• overall patient care.

In the next years, we will see more and more doctors working closely with software, which will boost largely available help for patients, not only in developed countries but even in the most remote regions.

A good example here is Bosch Vivascope,55 which is a cell-analysis platform using artificial intelligence to detect anomalies in biosamples. There are many regions of the world, where laboratory medicine is scarce. Sometimes there’s only one pathologist to 1.5 million people in a region. There’s often no one to examine blood for diseases and make a diagnosis. Two-thirds of the examinations are still carried out with a microscope, which is time-consuming...

Cybersecurity powered by AI

When it comes to our security in the digital world, AI is transforming both sides - it’s both defending and attacking us. Its malicious uses can be tracked to hackers trying to get into our bank accounts, stealing precious information from corporates and governments, or simply cracking our social media accounts.

On the other hand, we have better and more reliable defense systems which, through ultra-personalization, allow us to identify whether the agent is really what he claims to be, think about face-unlocking on phones, or determining who’s typing by the speed of typing.

Yet, on the other hand, ultra-personalisation goes together with a lack of privacy and surveillance capitalism. It’s often privacy versus security. Is there a way out?

More Cybersecurity AI startups are raising funds to defend us against hackers and malicious use of the software. Darktrace, a global machine learning company specialized in cyber defense...

Climate Change

Climate is becoming more of an issue each year, with the weather becoming more extreme in various parts of the world. We’ve already passed a point where restrictions will suffice, and we need to proactively change the way we manufacture, consume, and live. AI will play a role in our fight for the climate. Not only algorithms already provide better analytics and actionable insights, but paired with advances in robotics AI will be able to help us transit into renewable energies, reducing waste and emissions of greenhouse gas.

The most visible effect of pollution is plastic floating in the oceans. It ends up in animals’ stomachs leading to their death. This interrupts the food chain, influencing directly all other animals and humans alike and causing damage to the marine industry. Here comes Clear Blue Sea with FRED – the Floating Robot for Eliminating Debris. FRED is a solar-powered marine vessel capable of harvesting floating marine debris. Another...

Games and Reinforcement Learning

Video games are an excellent simulation environment for training machine learning algorithms. Because of games’ increasing complexity, games can be viewed as a model for our own reality. Learning how to play games is the first step to learn how to operate in real life. We value play as a way to learn for our children, and play is equally good for machines.

Reinforcement Learning (RL) techniques seem particularly well suited for games. The main focus of RL is to reward an algorithm when it completes a sub-task or moves in a good direction, and give it a penalty when it doesn’t. This is the closest to raising a child with a set of rules on which it builds its world-view model. Reinforcement Learning agents learn tasks by trial and error. They must balance exploration (new behaviors) with exploitation (repeating past behaviors).

Experiments in Reinforcement Learning within games like Go, DOTA 2, or Quake III Capture the Flag show that...

Hardware and beyond

In this final section on Artificial Intelligence trends, I want to talk about hardware and physical devices related to AI: AI chips, IoT, smart cities, quantum computing. Let’s have a look behind these buzzwords and see actual applications and opportunities.

It’s worth noting that machine learning by itself, that is, as a set of learning algorithms, is not useful until you’re able to provide it with enough computing power. That’s why advances in computing power and general computation techniques are influencing AI progress. In 2019 and 2020, we could see that through Transformers models with billion parameters trained on millions of texts (GPT-2, Megatron, Turing-NLG). AlphaGo and AlphaStar from Deepmind needed millions of dollars in cutting edge computing power to achieve human-level performance in Go and StarCraft II, respectively. We can expect new amazing applications of deep learning will require even more computing power. That...