How to assess Artificial Intelligence (AI) Startups (Part I)

This is Part I of a two-part series on “How to assess Artificial Intelligence (AI) startups”. Check out Part II here.

During the past few years, we’ve witnessed a massive growth in the number of so-called Artificial Intelligence (AI) startups. Both sides of the table, investors and entrepreneurs, have jumped into the frenzy with intent. 

This new trend responds to significant advancements in AI research. As real as these are, it’s essential to question the ongoing avalanche of companies using “AI” that has followed. 

Comparison of different AI systems for automatic generation of speech, and how they rank against human speech quality. Source: Deep Mind Wavenet Generative Model for Raw Audio.

What do we mean by AI Startup?

It’s easy to throw the word “AI” around, but it’s much harder to qualify what do we mean by that. Artificial Intelligence or AI, is a term that aggregates many, and varied mathematical approaches that try to emulate intelligent behavior.

When a company tells us that they’re an AI Startup, we should ask ourselves what exactly do they mean by that. Each AI algorithm is useful for a specific set of problems. There isn’t, as of this writing, a general AI tool that delivers magical answers for any challenge. 

Questions to ask: Because each algorithm is better at a specific set of problems, the first question we should ask a startup is what problem they are trying to fix with AI. 

These algorithms aren’t easy to develop and require extensive mathematical knowledge. Real breakthroughs are far and few between. Such is the case that the core math concepts behind most of the well-known AI algorithms are decades old. 

The reason why this math didn’t produce the expected “intelligence” was that, at the time, we lacked computational scale. Recent advances in both data and computational power have finally provided what AI needed for a breakthrough. 

Precisely because these algorithms aren’t easy to develop, most current “AI” practitioners, are really “AI” operators, not researchers. Deploying these algorithms, though, isn’t easy either. In most cases, they demand sophisticated expertise and extensive data and infrastructure. As complicated as it might be, there is still a big difference between designing an AI algorithm and executing it. 

“We individually reviewed the activities, focus, and funding of 2,830 purported AI startups in the 13 EU countries most active in AI – Austria, Denmark, Finland, France, Germany, Ireland, Italy, the Netherlands, Norway, Portugal, Spain, Sweden, and the United Kingdom. Together, these countries also comprise nearly 90% of EU GDP. In approximately 60% of the cases – 1,580 companies – there was evidence of AI material to a company’s value proposition.”

The State of AI: Divergence 2019 MMC Ventures Report

When assessing an AI startup, it’s critical to understand the difference. Using a tool and designing a tool are two very different things. Sometimes the tool designers have an edge; other times, it’s the efficient use of it that matters. 

Questions to ask: Are they a core AI company (i.e., developing their algorithms and math), or are they using an existing algorithm and applying it to a vertical problem?

The rise of Deep Learning

Nowadays, when a company mentions AI, chances are, they’re referring to either Machine Learning or Deep Learning algorithms. People use these terms interchangeably, which isn’t wrong, but it’s inaccurate. Deep Learning is a class of Machine Learning algorithms. To be more precise, it’s a type of algorithm closely related to what’s known as Artificial Neural Networks (ANN). The goals, algorithms, requirements, and subtleties of each Machine Learning approach can vary.

Questions to ask: When evaluating a startup, it’s important to pinpoint what type of Machine Learning algorithms are they using. The goal is to understand the needs or limitations of such an algorithm.

But why Deep Learning? With so many AI algorithms, startups have a vast toolkit at their disposal. However, during the last decade, the most popular type has been Deep Learning, and there is a reason for it. In September of 2012, a Deep Learning model called AlexNet achieved, for the first time in history, quasi-human image recognition abilities. AlexNet demonstrated not only that Deep Learning was more powerful than other methods, but that its performance could rival that of a human. 

The combination of AlexNet’s exceptional breakthrough and the advancement of hardware-specific improvements (i.e., GPU, FPGA, etc.) has generated a boom of research using Deep Learning methods. Since then, innovation and investment in the field have dramatically accelerated. 

Evolution and proliferation of Deep Learning models for image recognition purposes. Source: https://paperswithcode.com/sota/image-classification-on-imagenet

What is Deep Learning good at?

As I mentioned before, knowing what model a startup is using is essential. One of the reasons is because, so far, all AI algorithms are good at a limited range of problems. This ‘limitation’ means that to solve complex problems, researchers need more than one AI model. While Deep Learning is the new black, it’s rarely the ‘only’ algorithm in use. Nonetheless, it’s often at the core of most modern AI architectures. So the big question is, what is Deep Learning good at?

In general, Artificial Neural Networks, and by extension, Deep Neural Networks, are excellent at pattern matching. These algorithms are designed to learn, detect, and infer many different types of patterns. Several categories make good use of these properties.

Image Classification

AlexNet, the Deep Learning model that inspired the current AI wave, was designed for image recognition. It shouldn’t be too surprising that most Deep Learning models out there are also in the same category. 

There are many groundbreaking applications for computer vision. Some are straightforward, like classifying images. Think about all the current ‘Cancer detecting’ models trying to distinguish a normal MRI scan from a problematic one. 

In other cases, it’s about recognizing objects in an image. A good example is the growing crop of facial recognition companies. Many of these systems use a concatenation of Deep Learning models, each designed for a different purpose. 

Facial Recognition example: the first step of a facial recognition system is to identify a human in a picture. Most images (i.e., traffic cameras) show many objects, many of which aren’t human. Once it detects a human, it needs to isolate the face. The next step is to analyze and process facial expressions. These cues are then compared against a database in search of patterns. Each of these stages uses a different Machine Learning algorithm.

Face Recognition Based on Deep Learning. Source: Yurii Pashchenko, Technology Stream

Natural Language Processing

Another big field, pioneered by Google, is the use of Deep Learning to help computers understand language. One of the primary use cases is to be able to decide what a text is talking about. Text classification is a big problem, and Deep Learning has made this somehow easier. It shouldn’t be a surprise that Google uses such models to run their Search Engine (Google BERT). 

Language-related problems, though, can get much more complicated, like translations from and to different languages. Such systems make extensive use of Deep Learning to perform equivalences between words. 

Sketch of how Google’s Translator model does the pattern matching. Source: A Neural Network for Machine Translation, at Production Scale. Sep. 2016.
Google’s Deep Learning network’s invented language used for translating. Source: Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System. Nov. 2016.

Speech Recognition

Having talking robots has populated our imagination since the beginning of science fiction. Some will remember Hal, the T-800 or more recently, Her. Improvements in audio equipment and data bandwidth got us one step closer to that dream. But it’s been the use of Deep Learning algorithms, the ones that are making it a reality. We finally have a growing crop of voice AI assistants (Amazon’s Alexa, Apple’s Siri, Google Now, etc.) that can comprehend and speak our language. Some systems, like Google’s Duplex AI assistant, are borderline creepy. 

In these cases, the system uses Deep Learning to match audio to text (Automatic Speech Recognition, ASR) and then the generated text to a correct answer to give to the user. Each module, again, will have its independent Deep Learning model. 

Google Duplex AI demo. Google I/O 2018

Generation of content

While pattern matching is mostly used for classification purposes, it can also be used to generate utterly unique content. This category is becoming quite relevant as it’s enabling the generation of AI-generated articles, images, and videos. 

While there are valid use cases like having computers write news or even music, the fastest-growing use is that of fake news. Having the capacity to create phony content at scale is at the core of any disinformation campaign.

This category is appealing, especially for the creative arts, but it also holds deep ethical and moral questions. 

SYSTEM PROMPT (HUMAN-WRITTEN)
Miley Cyrus was caught shoplifting from Abercrombie and Fitch on Hollywood Boulevard today.

MODEL COMPLETION (MACHINE-WRITTEN, SECOND TRY)

The 19-year-old singer was caught on camera being escorted out of the store by security guards.

The singer was wearing a black hoodie with the label ‘Blurred Lines’ on the front and ‘Fashion Police’ on the back.

Scroll down for video

Shoplifting: Miley Cyrus was caught shoplifting from Abercrombie and Fitch on Hollywood Boulevard today (pictured)

The singer was wearing a black hoodie with the label ‘Blurred Lines’ on the front and ‘Fashion Police’ on the back

The singer was also wearing a pair of black-rimmed glasses, a black jacket, black jeans and black sandals.

She was carrying a pair of black and white striped gloves and a small black bag.

OpenAI GPT-2 Language Model Text Generator examples. Feb. 2019.

Questions to ask: Once we understand what algorithms a startup uses, we need to ask what they are using them for. Chances are we won’t be able to assess if this use is the best. Nonetheless, if we have a broad understanding, we can detect glaring misuses. We can ask the reverse question too. If we’ve identified a potential use for machine learning, and the startup isn’t using any, it might show their lack of commitment to AI.

This is Part I of a two-part series on “How to assess Artificial Intelligence (AI) startups”. Check out Part II here.

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