What is Artificial Intelligence?
Artificial Intelligence (AI) is a broad category that includes cutting-edge concepts such as deep learning. In general, AI is all about bringing aspects of intelligence to machines and having them perform tasks which can be natural and easy to humans, but extremely complicated to program. Moreover, an AI agent can execute such tasks autonomously and efficiently.
AI-driven solutions can be classified into two groups: general and narrow. General AI (also called strong AI) is what data scientists aim to develop in the future. It would be designed to solve broadly-defined problems intelligently thanks to sophisticated cognitive abilities and general experiential understanding of its environment. Today, that might sound like a science fiction scenario - but someday it’ll become a reality.
But AI is already changing the world as we know it - in the form of narrow AI, which focuses on performing specific tasks with incredible performance, often better than humans. For example, Pinterest uses a narrow AI solution for tagging images on the platform.
What data can you use for building AI solutions and how to do it?
Developers who want to create an AI-powered solution need to train algorithms on a set of diverse data - for example, a collection of images, text, or specific information like financial transactions or products viewed by users.
You can buy prepackaged data, take advantage of public crowdsourcing initiatives (such as the Amazon Mechanical Turk) or - when dealing with potentially sensitive data - hire private crowds of data science specialists able to help you out in data collection, identification, and labeling.
The dataset used for training needs to include a sufficient number of both positive and negative examples to help algorithms learn from it. For instance, if we want our algorithm to identify ugly cranes on pictures, we need to show it pictures both with cranes and without them.
The developer or data scientist may experiment with different algorithms before deciding which one is the best fit for the training data. But that’s not everything. We also need to provide the developer with a test set, a dataset used to test the model developed on the basis of the training data for evaluation and improvement.
What is the future of Artificial Intelligence and Machine Learning?
According to Gartner, “Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology-enabled service, thing, or application.” They also predict that by 2020, AI will become one of the top five investment priorities for at least 30% of Chief Information Officers.
Consumers are now increasingly used to digital assistants, self-driving cars, robots working in factories, and smart cities. AI made its mark on most industry sectors and continues to spread to new industries.
Experts predict that Machine Learning will grow at an increasing rate. Some people believe in the inevitable rise of that technology offered as a cloud-based service - the so-called Machine Learning-as-a-Service (MLaaS).
Ultimately, Machine Learning will keep on helping our machines to make better sense of data; both its context and meaning. And these powerful and actionable insights will make AI-based solutions indispensable for data-driven decision making among executives and project managers of the future.