What does a "journey" mean to you? At IBM, our long standing tradition of journey exploration has led humans to the moon and coined the term machine learning 50 years ago. Now we are helping organizations scale the ladder to AI to reap rewards in growth, productivity and efficiency with IBM Watson. This journey to AI mirrors the history of travel. In this article, I'll explain how IBM Cloud Pak for Data accelerates the journey to AI and delve into the ways AutoAI helps boost the speed of business returns.
AutoAI, a powerful, automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award yesterday at the AIconics AI Summit in San Francisco. Chosen by a panel of 13 independent judges, the AIconics awards recognize breakthroughs in AI for business. To share what went behind the development of AutoAI and how it accelerates time to value with data science projects, I interviewed one of our principal inventors: Jean-Francois Puget, PhD, a distinguished engineer for machine learning and optimization at IBM and a two-time Kaggle Grandmaster. What challenge led you to start developing AutoAI? Jean-Francois Puget: As data scientists, our work is a mix of applying general-purpose recipes and creating domain-specific insights.
In recent years, data-driven decision making has become critical to the success of corporations. There are many benefits of using technology for data-driven practices including the optimization of production and manufacturing, reductions in customer attrition, reductions in data redundancy, increased profitability, and the creation of competitive advantage. So data science has become popular as organizations embrace data-driven decision-making approaches. Data scientists need a wide range of skills including mathematics and statistics, machine learning and artificial intelligence (AI), databases and cloud computing, and data visualization. However, it is difficult to recruit enough data scientists, particularly with sufficient domain knowledge, such as banking, healthcare, human resources, manufacturing, and telco, for the tasks to be performed and decisions to be made.
Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither they trust the outputs. In this short paper, we build an experimental system AutoAIViz that aims to visualize AutoAI's model generation process to increase users' level of understanding and trust in AutoAI systems. Through a user study with 10 professional data scientists, we find that the proposed system helps participants to complete the data science tasks, and increases their perceptions of understanding and trust in the AutoAI system.
Robotics investments in January 2019 have crossed a minimum of $644 million worldwide, armed with a total of 25 robotics transactions. The $644 million raised in January is lower than the funding into this industry raised in December in tune of $652.7 million. One of the biggest investments in January that is $104 million Series A has been made into the Beijing Auto AI Technology Co. of China. Other notable investments in January 2019 into Robotics include the $100 million JV into Ekso Bionics Holdings Inc. and a $59.61 million Series B funding into China-based NASN Automotive Electronics Co. Here are the Top 10 Investments that ruled the Robotics Technologies space in January 2019.