Learn about machine learning, cognitive computing, and artificial intelligence, the impact of big data, and how machine learning is being given to the masses.
If you work in the area of artificial intelligence (AI) and cognitive computing, you might use buzzwords and phrases that, to others, might be perceived as confusing jargon. This article attempts to explain what these terms mean, how they relate to one other, and where they all fit along the AI and cognitive time continuum. I include a glossary of my top 20 useful AI/cognitive terms and advice on getting started on your AI/cognitive journey.
Machine learning grew out of the quest for AI. As an academic discipline, some researchers were interested in having machines learn from data, approaching the problem with various symbolic methods, as well as what were then termed “neural networks.”
Two important learning concepts to know about:
Around 1957, the “perceptron” was conceived: an algorithm for supervised learning of binary classifiers.
During the “AI Winter” of the 1970s and 1980s, there was pessimism in the AI community, reflected by the press and followed by severe cutbacks in funding and research. Three years later, the billion-dollar AI industry began to collapse.
During the 1980s, “backpropagation” caused a resurgence in ML research, followed by a shift to a data-driven approach in the 1990s. Scientists began creating programs for computers to analyze large amounts of data and draw conclusions or “learn” from the results.
In 2011, “Watson” competed on the Jeopardy! TV game, beating and outperforming the top contestants. Its natural language processing, predictive scoring, and models were key to its success.
Around 2015, there was a convergence of the many facets of ML and deep learning mentioned above. Cognitive computing is the ability of computers to simulate the human behaviors of understanding and thought processing. Open source, improved tools, demand for self-service PayGo analytics, cheap compute power, massive data ingest, scale-out processing, and flexible deployment options helped democratize cognitive computing, putting it in reach of the vast majority of the data science community.
Since the Jeopardy! game, AI has been applied across many industries from financial markets to help predict and prevent fraud in real time, to retail to help predict what customers will purchase next, to security and protection to help prevent attacks and crimes, to media to help tailor the viewing experience with targeted advertising and to healthcare to help doctors design cancer treatment plans.
But one thing was missing: making it consumable to the data science community regardless of skill level.
The IBM Data Science Experience (DSX) is a single unifying tool that allows multiple personas to collaborate across the data science lifecycle — from data preparation and ingest to ML model creation and training to deployment and management. DSX is suitable for all skill levels whether you prefer to use Notebooks or an intuitive step-by-step visual interface that applies cognitive techniques to choose the best algorithms for you. This IBM video on DSX provides more information.
Hopefully, this article helps readers understand how and when AI appeared and developed over time at a high level, how the different elements of AI, ML and cognitive relate to each other, as well as explaining some of the key terms we hear mentioned in this exciting industry. So, that’s the educational portion of this blog post. Your next step is to try machine learning for yourself by clicking here, which will take you to the IBM Data Science Experience.
