This is a self-funded project. The applicant should have or expect to gain at least an upper second class degree, or equivalent overseas qualification, in a relevant subject. If you have the correct qualifications and access to your own funding, either from your home country or your own finances, your application to work with this supervisor will be considered.

Many results in recent years established polynomial time learnability of various models via neural networks algorithms. However, unless the model is linear separable, or the activation is a polynomial, these results require very large networks -- much more than what is needed for the mere existence of a good predictor. In this paper we prove that SGD on depth two neural networks can memorize samples, learn polynomials with bounded weights, and learn certain kernel spaces, with near optimal network size, sample complexity, and runtime. In particular, we show that SGD on depth two network with $\tilde{O}\left(\frac{m}{d}\right)$ hidden neurons (and hence $\tilde{O}(m)$ parameters) can memorize $m$ random labeled points in $\mathbb{S}^{d-1}$.

Deep neural networks (DNNs) can easily fit a random labeling of the training data with zero training error. What is the difference between DNNs trained with random labels and the ones trained with true labels? Our paper answers this question with two contributions. First, we study the memorization properties of DNNs. Our empirical experiments shed light on how DNNs prioritize the learning of simple input patterns. In the second part, we propose to measure the similarity between what different DNNs have learned and memorized. With the proposed approach, we analyze and compare DNNs trained on data with true labels and random labels. The analysis shows that DNNs have \textit{One way to Learn} and \textit{N ways to Memorize}. We also use gradient information to gain an understanding of the analysis results.

IBM believes 100% of jobs will eventually change due to artificial intelligence, and new empirical research released last October 30 from the MIT-IBM Watson AI Lab reveals how. The research, The Future of Work: How New Technologies Are Transforming Tasks, used advanced machine learning techniques to analyze 170 million online job postings in the United States between 2010 and 2017. It shows, in the early stages of AI adoption, how tasks of individual jobs are transforming and the impact on employment and wages. "As new technologies continue to scale within businesses and across industries, it is our responsibility as innovators to understand not only the business process implications, but also the societal impact," said Martin Fleming, vice president and chief economist of IBM. "To that end, this empirical research from the MIT-IBM Watson AI Lab sheds new light on how tasks are reorganizing between people and machines as a result of AI and new technologies."

Artificial intelligence has helped archaeologists uncover an ancient lost work of art. The Nazca Lines in Peru are ancient geoglyphs, images carved into the landscape. First formally studied in 1926, they depict people, animals, plants, and geometric shapes. The formations vary in size, with some of the biggest running up to 30 miles long. Their exact purpose is unknown, although some archaeologists think they may have had religious or spiritual significance.

IBM has always believed that 100% of jobs will ultimately change due to the impact of AI. Recent empirical research conducted by the MIT-IBM Watson AI Lab provides insights into the prediction and explains how it's going to happen. The joint research by Massachusetts Institute of Technology and IBM scrutinized the probable applications of Machine Learning in 170 million online job postings between 2010 and 2017 and came up with a report "The Future of Work: How New Technologies Are Transforming Tasks." The research examined the impact of Artificial Intelligence on employment and inferred that the result will be a significant decrease in the number of tasks. It additionally stated that work that would require "soft skills" would be given more focus on.

Woodside and IBM will work together to re-imagine the way work is done using next-generation technologies such as artificial intelligence (AI) and quantum computing to help Woodside realize its vision of an "intelligent plant." Announced today at IBM's Cloud Innovation Exchange in Sydney by Woodside CEO Peter Coleman and IBM Chairman, President and CEO Ginni Rometty, the collaboration will include Woodside becoming a member of the MIT-IBM Watson AI Lab, which is a collaborative industrial-academic laboratory focused on advancing fundamental AI research. Woodside will also join the IBM Q Network, making it the first commercial organization in Australia to join IBM's quantum computing network. Woodside and IBM will use quantum computing to conduct deep computational simulations across the value chain of Woodside's business. During the past five years Woodside and IBM have worked together to implement cognitive solutions, enabling advances in health and safety, planning and operations, and project engineering.

The latest report titled global Artificial Intelligence Platform Software market includes the comprehensive study of the present market scope and based on the research that is being carried out the analysts at The Research Insights state that the newest developments that are presently affecting the changing scenario products and services that have high rankings and great feedback are described wisely. The Artificial Intelligence platform provides tools and technologies to build applications with AI-rich capabilities. The algorithms used for formulating the AI platform provide logical models for application developers to fabricate various innovative applications with capabilities, such as speech and voice recognition, text recognition, and predictive analytics. The factors likely to drive the Artificial Intelligence platform market are the substantial increase in data generation, high demand for AI-based solutions, the need to enhance customer experience, and the increasing operational efficiency & reduced cost that AI platforms offer. Among end users, the BFSI segment is projected to have the largest share, while healthcare is expected to have the highest growth rate during the forecast period.

Climbing the AI ladder: How does that affect my business? Businesses love the idea of putting data to work. Building and scaling AI with trust and transparency -- sounds great, right? As enterprises adopt machine learning to streamline customer service and remedial tasks, their employees can provide better customer experience while freeing themselves up to work on more interesting problems. IBM leads the industry in empowering enterprises to accelerate the journey to AI.

Rapid advancements in the field of artificial intelligence (AI) are uniquely poised to transform entire occupations and industries, changing the way work will be done in the future. It is imperative to understand the extent and nature of the changes so that we can prepare today for the jobs of tomorrow. New empirical work from the MIT-IBM Watson AI Lab uncovers how jobs will transform as AI and new technologies continue to scale across business and industries. We created a novel dataset using machine learning techniques on 170 million U.S. job postings. The dataset and research, The Future of Work: How New Technologies Are Transforming Tasks, allow us to extract key insights into how AI is shaping the future of work.