The defining characteristic of today’s AI in comparison to that of five, ten, or twenty years ago is the ability to operate without being explicitly programmed.  These systems learn how to use data in such a way that they can make inferences, draw conclusions, and undertake actions on their own, rather than being told how in their underlying code. As more and more data flows through, the machine becomes smarter, and its outputs constantly improve.

But despite artificial intelligence becoming so much stronger, many companies still don’t know the best ways to get the most out of such a powerful technology. So many companies have specific problems that need addressing but don’t have the resources it takes to build a custom AI solution.  Even some of the biggest companies in the world, who have more than enough capability to invest long-term in artificial intelligence, would need to get the right people in place and very carefully define the problems they need to solve, both now and in the future, to get the most out of their investment.  AI is built to learn, but it is still only capable of learning about the questions which the programmer proposes.

Business leaders must think more broadly about the potential of AI and its functionality across industries while also helping specify how their enterprise might specifically gain from the unique benefits afforded by machine learning and deep learning. We will also discuss the risks and challenges associated with AI, the proper development of an AI strategy, and the languages and programs such as TensorFlow and Keras that will assist in building the ideal AI platform for your business.

Building any AI system is not a simple task, and building one that can address specific needs within a company’s infrastructures is even less so.  Businesses will need to seek consultants and partners that have previously built custom AI programs, capitalizing on their knowledge and experience while also drawing inspiration on how to create something unique. AI and machine learning systems require a large amount of time, coordination, capital, and organizational buy-in, but the investment is crucial not just because it will improve a company’s efficiency or profitability, but because it will allow you to keep up in a market that is increasingly reliant on AI every day.

Although the term artificial intelligence has been in popular culture for years now, very few people can actually define it well or understand the process within the technology, ignoring the actual code and programming that goes into it.  Many people imagine a smiling robot (or an evil one) who has developed the ability to communicate and “think” commensurate with that of a human.  Part of the confusion comes from differing definitions offered by famous scientists such as Alan Turing, and part of it comes from a lack of consensus on a term as seemingly simple as “intelligence.”

Artificial intelligence can be defined as having three specific characteristics, as outlined by the Brookings Institute: intentionality, intelligence, and adaptability.

Because AI is designed to reach conclusions and assist in decision-making, they must then be designed with a specific kind of outcome in mind; the machine will learn and provide better outputs over time, but they must be given a framework through which to answer the question. For example, a self-driving car must take in all the information around it to stay in its lane and avoid colliding with other cars or street signs. If the car were to take in that information and then make a determination instead as to the condition of the road, that would not be a useful tool.

However, as these machines become faster and more efficient in their observations, the lens through which they can view the world grows exponentially. AI has evolved from breaking codes to playing complex games such as chess and Go to now handling business transactions and ensuring we waste as little money or energy as possible.

AI must also be intelligent, and by that we mean it must be able to decipher trends and draw conclusions on its own. This is where machine learning comes into play, which we will talk about more in depth shortly. AI is meant to spot patterns in data that cannot be processed by the human brain, patterns which are then evaluated using analytical data methods.  The responsibility of writing the code, building the algorithms, and by proxy determining the eventual effectuality of the machine learning program falls more and more to the computer programmers who must build these algorithms in such a way that will guarantee peak efficiency in analyzing the data as well as turning that analysis into real-world decisions and outcomes.

Finally, adaptability allows AI to make its own changes as circumstances change or if it finds results that necessitate adjustments. As the world around it changes, AI must be able to adjust accordingly as new possibilities could arise. One example where this is most relevant is in the military, where constantly evolving capabilities of manpower or weaponry or wide-scale strategy correspondingly affect the landscape upon which an AI system would operate.  By building systems that can see this changing landscape and adapt itself, you save human labor hours, you increase program efficiency, and you make your AI more effective and accurate in the long-term, as any adjustment the machine learns to make to itself will be more in tune than an adjustment made by a person.

But artificial intelligence as generally defined has been around for years; only over the last several has AI as we have just defined become feasible, and that recreation of AI is possible thanks to the recent breakthroughs in machine learning. Machine learning, as defined by AI research group Emerj, is “the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.”