Practical AI and how it is done

Artificial Intelligence in business is practical. When you think about neural networks, don’t think about abstract mathematical structures, but rather computer systems that need data to learn business processes and how to operate within them.

Data Science is not a real science, it’s an experimentation domain, where you need to constantly adjust, test, build prototypes from scratch, and rebuild what you have. It’s a framework for approaching problems rather than a specific set of tools. This paradigm of using neural networks, statistics on steroids, is what makes AI both practically and theoretically complex, with such a broad range of applications, which we’re going to cover in the next chapter.

So how Data Science or Artificial Intelligence is currently done? You could split the actual work into two parts, connected strongly with each other:

• implementation,
• research.

Implementation phase is focused...

Research in Artificial Intelligence

The research community in Artificial Intelligence can be split into three divisions:

• machine learning community
• ethics and social community
• business community

Machine Learning community is concerned primarily with research questions related to building machine learning models: from architecture through data to implementations. PhD in computer science or STEM field is necessary to participate actively in it.

Ethics and social community focuses on social ramifications of doing AI research and applying it in practice: from legislations to important questions or limits on what should be the goal of AI research. People in this community often work in social departments of universities, think tanks, or public institutions.

Business community focuses on applying cutting-edge research to business problems. Those may include manufacturing, drug design, cybersecurity, video games, and others. Researchers here work mostly at research...

Open-source community

Important from a business perspective and still largely underused by the business is the open-source community within machine learning. Much of research is available for free on GitHub, a repository of code, and can be picked up and used jointly with other pieces to build something unique you need. Never making a prototype was so fast and cheap as now. The open-source community is also an excellent source for potential hires as it accurately shows what a given person is capable of by just looking at his or her code repository.

Business-wise supporting the open-source community has many advantages: access to the talent pool, staying informed about current research. Moreover, it can bring business leads. Recall the model of Red Hat which was responsible for maintaining Linux and then earning money via support and customisations. In the end, Red Hat was acquired by IBM in one of the largest tech acquisitions to date at massive $34 billion closed in 2019.

...

From research to applications

Having discussed how research is done in AI, it’s now time to focus on applications. Assuming you already have a data science team in place and preliminary research on a problem you want to solve done, the next step is to gather and clean data. This process can be short if most of your business is digital with easy access to data, or long and painful if you have many sources to look at and data is far from clean (say, surveys of customers done in various formats). If that’s the case, preprocessing is a task that would need a separate team to complete. It’s especially essential for all the later work, so don’t ignore cleaning data.

Applying research to business applications means using machine learning models on data coming from your business and measuring how well they behave compared to how you usually solve the problem at hand (e.g. time spent on a business process, marketing/sales, number of relevant leads). After receiving...

Cons of using AI

Using Artificial Intelligence solutions can create three risks.

Firstly, the machines may have hidden biases due to the data provided for training. For instance, if a system learns which job applicants to accept for an interview by using a data set of decisions made by human recruiters in the past, it may inadvertently learn to perpetuate their racial, gender, ethnic, or other biases. These biases are hard to detect as they won’t appear explicitly, but rather be embedded in the solution where other factors are considered.

The second risk is that, unlike traditional software engineered systems built on explicit logic rules, neural network systems deal with statistical truths rather than literal truths. Thus it much harder or sometimes impossible to prove that the system will work in all cases — especially in situations that weren’t represented in the training data. Lack of verifiability can be a concern in critical applications, such as controlling...