Research

What is a real AI company?

These days AI is an over-hyped buzzword, and not many people really know what real AI is.
By
Raffle
TABLE OF CONTENTS

AI has been around since the 50s, but it’s only recently that companies and investors alike are paying attention to it. Why is AI so hyped these days? And how can you see beyond the buzzwords?

State of AI

Today AI is an over-hyped buzzword, but it hasn’t always been like that. Only a few years ago, saying your company works with AI would not exactly excite. Deep Mind CEO Demis Hassabis said, “7 years ago when you would have said the words AI to a Venture Capitalist, they would roll their eyes at you. Today, they will throw 10 million dollars at you”. The business world is embracing AI with open arms, but the problem is that not many people know what real AI is. Companies and VCs struggle to evaluate whether a company is working with the technology or just cashing in on the hype.

A survey from London’s VC firm MMC found that 40% of Europe’s startups that classify as AI companies don’t exploit the field of study in any material way. David Kelnar, head of research at MMC, told Forbes, “we looked at every company, and in 40% of cases, we could find no mention or evidence of AI” he adds that “companies that people assume and think are AI companies, are probably not.”

So how can you distinguish if the solutions being sold to you are really based on AI? First of all, you need to understand what AI can actually do for you. You can read more about it in our previous article. Second, you need to know a bit about the historical context of the field. Though AI has been around for over 60 years, it’s only in the past few years that significant developments have been made in creating anything resembling artificial intelligence.

A bit of history…

The first intelligent machine was created by Alan Turing during WW2. This machine called the Bombe would crack the ‘Enigma’ code, used by German forces to send encrypted messages. Then in 1956, at the Dartmouth Conference, the term ‘Artificial Intelligence' was first adopted.

Since then, due to a lack of computational power, AI has become less popular and went through several ‘AI winters.’ It wasn’t until the late 1990s that AI once again received significant attention. In 1997 IBM’s Deep Blue defeated Garry Kasparov, the reigning world chess champion. Thereafter, thanks to the exponential growth of data, computing power, and improvements in hardware, AI has been slowly picking up pace.

Today, we are at a point where massively funded research is done in the field, and breakthroughs in, amongst others, natural language processing; image recognition and generation; computer vision, and reinforcement learning, are rapidly shaping the industry and opening up new possibilities. Still, there are many ways to create artificial intelligence. Some are very intelligent — others not so much.

How do companies build AI?

AI can be a pile of if-then statements or a complex statistical model built using deep neural networks. The if-then statements are essentially just rules programmed by humans, sometimes, they are called rules engines or expert systems, but collectively they are known as Good, Old-fashioned AI (GOFAI).

These systems might be useful for conducting repeated tasks but have little to do with actual intelligence. They can automate processes but don’t self-learn or improve without human intervention. You all know examples of this technology — most chatbots and accounting systems are built on it.

Lack of robustness to the variation one sees in naturally generated data such as text is also a problem for GOFAI. And this way of building AI is also quite limited in the scope of data it can process. On the other hand, machine learning and neural networks require little to no human intervention.

These programs alter themselves, are dynamic, and adjust based on the data they are exposed to. Thanks to this, they assist people in their work and simplify daily tasks. Still, they might require quite a lot of data to reach sufficient performance.

The first red flag is if you are being offered a solution that requires you to do manual work and/or hire people to service it. If that is the case, you are dealing with a ‘Good, old-fashioned AI’ case.

MMC’s report found that 26% of the startups in the study said they use AI to power chatbots. But it is hard to evaluate how much of a benefit the technology brings to their customers. Chatbots are often hard to navigate and more annoying than useful — simply used to cut the costs of human employees. 

The reason is that, though called ‘AI,’ they are rule-based systems incapable of real understanding (see illustration above). However, the recent developments in the AI field have allowed for more sophisticated generations of dialogue-based tools that use Natural Language Processing (NLP) and Deep Learning to understand meaning and answer in natural text.

By transferring text into vector representations, holding numerical values, makes it possible to process text in entirely new ways. Combining NLP and transfer learning (applying pre-trained models to data) opens doors to new possibilities for text generation, understanding, and translating.

In the case that your company is looking to invest in AI, make sure that the solutions you choose are based on state-of-the-art developments that can be leveraged in the future. Using if-then rule-based systems programmed by humans will soon become completely obsolete.

Where is AI today?

Natural Language Processing

In 2018, we saw remarkable breakthroughs in language and text. OpenAI’s GPT-2 generates stories based on short descriptions.

The model is trained to predict the next word but, unlike similar models, does so while maintaining the context of the whole text, modeling the text in a way that it has representation from all the previous input.

Facebook research has expanded its LASER (Language Agnostic Sentence Representation) Toolkit to work with 93 languages across 28 different alphabets. This model delivers strong results in cross-lingual document classifications and is revolutionizing translations.

In the future, we expect that pertained language model embeddings will be widely leveraged in state-of-the-art models. Other examples of important text representation tools are ELMO, BERT, and XLNET.

Computer Vision

One of the most popular fields in the deep learning space — computer vision — also advanced massively. Whether for image or video, new frameworks and libraries are making computer vision tasks easier. BigGANs are now capable of high-fidelity image synthesis, producing nearly unrecognizable images from real photographs. We have all seen Deepfake videos of world leaders or art pieces that came to life. In the future, we can expect to see this technology being used across a wide range of fields, such as holograms, teaching, and filmmaking.

Deep mind’s AlphaZero is the new, improved, and more generalized version of AlphaGO and AlphaGOZero. Though its predecessors were champion game-playing AIs, they were still taught the games by studying humans playing. AlphaZero, on the other hand, taught itself from scratch by playing against itself. The technology, not constrained by human tactics, has developed novel strategies for playing GO, Chess, and Shogi and forms its evaluations of the game. 

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