Artificial intelligence (AI) is no longer a futuristic concept, but it’s not without challenges. Despite the hype, the benefits of using AI are often limited if the technology is not properly understood and integrated.

Venture capitalist Alan Patricof says that many companies are rushing to launch AI-focused businesses. But he cautions that AI is the “flavor of the month” and suggests taking a long-term view.

Reactive Machines

Many people outside technology circles know of AI through buzzwords such as machine learning and natural language processing. But they may not realize that artificial intelligence is actually a broad field of study with four distinct types: reactive machines, limited memory, theory of mind and self-aware AI.

Reactive machines are the simplest form of AI, which are programmed to react to specific inputs in real time without relying on past experiences to make future decisions. These machines are able to perform the tasks they’re programmed for but lack imagination and can be easily fooled by humans. Spam filters and Netflix recommendation engines are everyday examples of reactive machines. Other well-known examples include IBM’s chess-playing supercomputer Deep Blue and Google’s game playing Artificial intelligence, AlphaGo.

This type of AI isn’t very useful, but it has some advantages. Reactive machines can handle high-volume, repetitive tasks quickly and reliably, making them perfect for things like automated teller machines or traffic lights. They’re also extremely effective at solving problems such as detecting fraud or finding the best route to a destination.

The next step up from reactive machines are limited memory AIs, which can learn from previous experience but still don’t have the ability to form their own memories. Most modern AIs, such as image recognition systems, fall into this category. These systems can take in large amounts of data to build a model that will help them identify objects or scenes, but they don’t remember this information from one situation to the next.

Another advantage of this type of Artificial intelligence is its ability to understand complex human behavior, which can be difficult for a computer to replicate. This allows it to better interact with the world and potentially even make human-like decisions.

Theory of mind AIs can identify and respond to emotions in other people, as well as understand the motivations and thoughts behind their actions. This type of AI is currently very limited in scope, but it’s a good example of how advanced future AI could be. Finally, there is self-aware AI, which is the most advanced and closest to what humans are. Creating this level of AI is incredibly difficult and it’s likely that these types of machines will only become fully functional in the far future, but we should be prepared for them when they arrive.

Cognitive Machines

Cognitive Machines are designed to learn on the fly. They analyze and interpret the environment they are in and change their behavior accordingly. They are able to understand the context of their current activity, identify the best course of action and even make suggestions for how it should be approached. They have a massive repository of data to draw upon and can reconcile ambiguous and self-contradictory information to provide an optimal solution.

This is a new area of AI that has recently gained traction. There are now powerful tools, some of which are open source (like TensorFlow, developed by the Google Brain Team), that allow developers to create cognitive technology faster and easier.

One of the most interesting examples of this is the DeepMind Artificial intelligence system AlphaGo, which used reinforcement learning to develop a narrow AI to play Go, the ancient Chinese strategy game for two players. It was able to beat a professional human player on a full-sized board, an unprecedented achievement for the field of Artificial intelligence.

The ability of this technology to interact with humans in a natural manner and replicate the process of thinking is one of its most compelling advantages over AI. However, it is important to note that this does not mean that these machines are smarter than humans. Humans are unique in that they have a set of special capabilities like intuition, compassion, design and value judgments as well as the power to do fast and large scale math and reasoning. Machines cannot have these capabilities, but they do have a lot of computational power and the ability to quickly check facts.

Cognitive computing is a new area of AI that uses this power to transform business processes and enhance the customer experience. It aims to create a synergy between man and machine, instead of creating an adversarial relationship where one will replace the other. This approach will not only free up time for people to focus on other tasks, but also allows them to perform jobs with greater skill and creativity. This will enable the workforce to reshape their careers and shift from manual tasks to more information-centric ones that are more aligned with their skillsets.

Black Box AI

Black box AI models are complex machine learning algorithms that don’t have a transparent decision-making process. They hide their inner logic from users and engineers, making it difficult to understand what’s causing them to make certain decisions. This is a major issue because it makes it hard to trust their results. In addition, black box Artificial intelligence can be dangerous when it’s used to make high-stakes decisions such as handing out prison sentences, determining credit scores and deciding treatment options at hospitals. These erroneous decisions can cause real harm to individuals and companies.

One of the reasons that black-box AI is so popular is because it’s more affordable than developing interpretable AI systems. According to Rudin, companies can spend a fortune on training and computing power to train black box algorithms but it doesn’t take the same amount of money to develop interpretable systems. It’s also more convenient to use black-box AI systems because they don’t require domain expertise or talent to operate.

To help solve the problem, there has been a rise in research on so-called explainable AI systems. However, Rudin argues that this approach is not the answer. Instead, she recommends that researchers focus on developing AI models that are inherently interpretable.

While achieving this can be challenging, it is possible. For example, she cites the work of a researcher named Senen Barro who developed an AI model that can explain its decisions to humans using natural language. The key is to find a way to attach an explanatory algorithm to the model that can ask the right questions of the data it’s working with.

Some ML models are already interpretable, including decision trees and linear regression. These types of models can tell you what data goes in and what comes out, but they won’t be able to tell you how the millions or billions of parameters combine to produce a particular result. Other ML models, like deep neural networks, can have thousands or even millions of weights that are hard to track, which makes them challenging to understand. Nevertheless, many developers are praising the functionality of these new interpretable AI systems. They are highlighting their ability to automate tasks, such as extracting text from videos, speed up their code writing and improve the accuracy of their coding.

Explainability

Explainability is one of the biggest focuses in AI right now because it offers a way to build trust in AI models and systems. Customers, regulators and the public at large want to feel confident that AI models making impactful decisions are doing so accurately and in a fair way. Explainability helps address these concerns and can also lead to more positive financial returns for companies.

Unlike more simple machine learning models that analyze structured tabular data, explainable AI software models often utilize vast amounts of unstructured data such as natural language or raw images. Interrogating the decisions made by these types of models can be much more difficult than with a model that uses only structured data. Explainability helps solve this problem by providing a transparent report that explains how and why the Artificial intelligence system arrived at its decision.

One of the most important benefits of explainability is detecting flaws and biases in an AI model or data which can then be corrected and improved upon. This not only builds trust for all users but can also be a key to increasing model performance by eliminating any inaccuracies that may have been introduced during the training process or due to environmental factors.

Another major benefit of explainability is that it can help to increase user adoption of an Artificial intelligence system. For example, sales teams are more likely to follow recommendations made by an AI application if they know why the recommendation was made than if those same suggestions came from a black box that they don’t understand. This is particularly true when a recommendation could have serious consequences for the end user, such as suggesting they pursue a graduate school admission that would significantly alter their career path.

While IBM gained notoriety for its artificial intelligence supercomputer Watson’s win over human contestants on the game show Jeopardy more than a decade ago, the company now wants to make the technology more accessible by incorporating explainability into its algorithms. The goal is to allow teams to reverse-engineer the factors driving predictive outcomes for more complex AI models using simpler, better-understood statistical methods such as logistic regression.