This article presents a new design for autonomous artificial intelligence (AI), based on the state-of-the-art algorithms, and describes a new autonomous AI system called AutoAI. The methodology is used to assemble the design founded on self-improved algorithms that use new and emerging sources of data (NEFD). The objective of the article is to conceptualise the design of a novel AutoAI algorithm. The conceptual approach is used to advance into building new and improved algorithms. The article integrates and consolidates the findings from existing literature and advances the AutoAI design into (1) using new and emerging sources of data for teaching and training AI algorithms and (2) enabling AI algorithms to use automated tools for training new and improved algorithms. This approach is going beyond the state-of-the-art in AI algorithms and suggests a design that enables autonomous algorithms to self-optimise and self-adapt, and on a higher level, be capable to self-procreate.

I recently had the opportunity to discuss current IBM artificial intelligence developments with Dr. Lisa Amini, an IBM Distinguished Engineer and the Director of IBM Research Cambridge, home to the MIT-IBM Watson AI Lab. Dr. Amini was previously Director of Knowledge & Reasoning Research in the Cognitive Computing group at IBM's TJ Watson Research Center in New York. Dr. Amini earned her Ph.D. degree in Computer Science from Columbia University. Dr. Amini and her team are part of IBM Research tasked with creating the next generation of Automated AI and data science. I was interested in automation's impact on the lifecycles of artificial intelligence and machine learning and centered our discussion around next-generation capabilities for AutoAI. AutoAI automates the highly complex process of finding and optimizing the best ML model, features, and model hyperparameters for your data.

Artificial Intelligence has been consistently one of the hottest topics year-round since the last decade. And there doesn't seem to be any sign of that changing anytime soon. Keeping that in mind, we have compiled a list of the TOP-10 stories that have been the talk of the town of 2021. So without further ado, here is the first one. Automating AI has been growing in popularity for the last few years.

In this code pattern, learn how to use AutoAI to automatically generate a Jupyter Notebook that contains Python code of a machine learning model. Then, explore, modify, and retrain the model pipeline using Python before deploying the model in IBM Watson Machine Learning using Watson Machine Learning APIs. AutoAI is a graphical tool available within IBM Watson Studio that analyzes your data set, generates several model pipelines, and ranks them based on the metric chosen for the problem. This code pattern shows extended features of AutoAI. More basic AutoAI exploration for the same data set is covered in the Generate machine learning model pipelines to choose the best model for your problem tutorial.

As an increasing number of organizations drive AI-powered digital transformation, several key trends in operationalizing AI are emerging. Growth leaders are separating themselves from growth laggards by using AI and machine learning (ML) in modern application development. Below are some statistics provided by 451 Research: Leaders invest in models for digital transformation: More than half the digital transformation leaders adopted ML compared to less than 25 percent of laggards. Furthermore, 62 percent of enterprises are developing their own models. Prevalence of DevOps increases the demand for automation: 94 percent of enterprise companies have now adopted DevOps. Models are becoming integral to the development of enterprise apps—requiring continuous, synchronized and automated development and deployment lifecycles. Data science and DevOps/app teams collaborate more: In 33 percent of enterprises, the data science/data analytics team is the primary DevOps stakeholder. An increasing number of application developers are becoming interested in data science and AI, and many have already learned the fundamentals…

For the fifth consecutive year, IDC ranked IBM the #1 market share leader in AI software platforms for 2019. In the IDC report, Worldwide AI Software Platforms Market Shares, 2019: The Battle Has Begun (doc #US46652020, July 2020), IDC valued the AI software platform market at USD 3.5 billion in 2019, a near-30% increase over the prior year. And despite a crowded landscape of competitors, IDC finds IBM leading the field among the largest AI platform players with an 8.8% share. While COVID-19 forces companies worldwide to reconsider business as usual, the accolades can wait; there's no time for a victory lap when work remains to accelerate the COVID-19 economic recovery with Data and AI. With IBM Watson positioned as the business world's first choice in AI software platforms, four competitive differentiators distinguish it from the competition.

It’s been said that data is the most valuable resource on the planet. But most companies aren’t getting the maximum value out of their data. If you look at the top three things that are really needed in the marketplace, it’s really been around defining a data strategy, filling the skill shortage and how to operationalize and industrialize your AI. In fact, while AI is helping companies gain competitive advantage in a growing range of industries, 51 percent find optimizing, sustaining and expanding AI capabilities challenging, notes Forrester. Why is that? The fact is that, in a business context, AI is fairly new. It can also be a bit intimidating. So, I’d like to share some quick thoughts how to build an AI strategy and how to measure its success. Formulating your AI strategy Ultimately, of course, the success of any AI project has to be measured in dollars and cents. As you formulate an AI strategy, it’s important to identify and prioritize…

Strategic investments in AI can be a game changer. To fulfill the promise of AI, organizations are now tackling skill-set gaps, deployment and governance processes. In particular, businesses are seeking an alternative where citizen data scientists can quickly get started, and expert data scientists can speed experimentation time from weeks and months to minutes and hours. They need a multimodal data science and AI environment where data and analytics specialists collaborate with other experts and optimize model performance end-to-end. AutoAI is available within IBM Watson Studio with one-click deployment through Watson Machine Learning.

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI--but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

Dakuo Wang is a Research Scientist at IBM Research AI, Cambridge, Massachusetts. His research lies in the intersection between human-computer interaction (HCI) and artificial intelligence (AI). He is now leading a team of researchers, engineers, and designers to conduct research and design user experience for IBM AutoAI, a solution to automate the end-to-end machine learning pipeline. From studying how users work with various AI systems such as automated machine learning (AutoML/AutoAI), chatbots, and clinical decision support systems (CDSS), he proposes "Human-AI Collaboration" as a new framework to examine and design AI systems to work together with humans. Before joining IBM Research, Dakuo Wang got his Ph.D. and M.S. in Information and Computer Science from the University of California Irvine, a Diplôme d'Ingénieur (M.S.) in Information System from École Centrale d'Électronique Paris, and a B.S. in Computer Science from Beijing University of Technology.