Artificial Intelligence (AI) is much in the news these days. As a concept, AI seems completely unrelated to the field of law. Although, AI and Law are intricately intertwined and are becoming more so each day. In this lecture, Professor Harry Surden – a former software engineer and leader of the emerging interdisciplinary field of AI and Law – will explore: What is Artificial Intelligence? How is law affecting Artificial Intelligence?

According to the perceptual symbol hypothesis (Barsalou, 1999), word concepts trigger mental re-enactments of perceptual states and actions. While many studies have shown how word concepts modulate sensori-motor responses, it is less well known how sensori-motor actions influence access to word concepts in memory. Here, we investigated how well English words with strong horizontal or vertical associations are retrieved from memory dependent on how they are presented during encoding (i.e., horizontally or vertically printed). Initial pre-testing of 129 candidate words yielded 43 words with a strong horizontal association (e.g., floor, beach, border, etc.) and 51 words with a strong vertical association (e.g., tree, crane, bottle, etc.). These were quasi-randomly compiled into 160 'crossword arrays', each containing 5 horizontally and 5 vertically printed items drawn from the horizontal association word set, as well as 5 horizontally and 5 vertically printed items drawn from the vertical association word set.

It began with a pulled muscle. Each day after school, as the sun sank dusky purple over the hills of my hometown, I'd run with my track teammates. Even on our easy days, I'd bound ahead, leaving them behind. It wasn't that I thought myself better than them--it's that when I ran fast, and focused on nothing but the cold air burning my lungs and my feet pounding, my normally anxious thoughts turned to white noise. I limped a little, and then tried running again: sharp, hot pain radiated down my thigh. Panic flooded me, as I imagined weeks without running: weeks without a predictable break from my own thoughts, weeks immersed in adolescent loneliness.

In many academic settings, medical students start their scientific work already during their studies. Like at our institution, they often work in interdisciplinary teams with more or less experienced (postgraduate) researchers of pharmaceutical sciences, natural sciences in general, or biostatistics. All of them should be taught good research practices as an integral part of their education, especially in terms of statistical analysis. This includes reproducibility as a central aspect of modern research. Acknowledging that even educators might be unfamiliar with necessary aspects of a perfectly reproducible workflow, I agreed to give a lecture series on reproducible research (RR) for medical students and postgraduate pharmacists involved in several areas of clinical research. Thus, I designed a piloting lecture series to highlight definitions of RR, reasons for RR, potential merits of RR, and ways to work accordingly. In trying to actually reproduce a published analysis, I encountered several practical obstacles. In this article, I focus on this working example to emphasize the manifold facets of RR, to provide possible explanations and solutions, and argue that harmonized curricula for (quantitative) clinical researchers should include RR principles. I therefore hope these experiences are helpful to raise awareness among educators and students. RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for the public profiting from research findings.

Open Deep Learning and Reinforcement Learning lectures from top Universities like Stanford University, MIT, UC Berkeley. This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Experience in Python is helpful but not necessary.

Techfest is the annual science and technology festival of Indian Institute of Technology Bombay.[1] It also refers to the independent body of students who organize this event along with many other social initiatives and outreach programs around the year. Techfest is known for hosting a variety of events that include competitions, exhibitions, lectures as well as workshops. Started in 1998 with the aim of providing a platform for the Indian student community to develop and showcase their technical prowess, it has now grown into Asia's Largest Science and Technology Festival[2] with a footfall of 1.75 lakhs in its latest edition.[3][4][5] The activities culminate in a grand three-day event in the campus of IIT Bombay which attracts people from all over the World, including students, academia, corporates and the general public.[6] The very first edition of Techfest was in 1998. The underlying spirit of Techfest was "to promote technology and scientific thinking and innovation" a motto that has been followed by every Techfest since. Techfest '98 also set the broad outlines of Techfest in the form of competitions, lectures, workshops, and exhibitions which went on to become a standard feature at every Techfest. Entrepreneurship also made an appearance in the 1999 and 2000 editions. Technoholix--Techfest in the Dark, showcasing technological entertainment at the end of each day as well as the hub of on the spot activities, made their debut during these years. Techfest 2001-2002 saw the incorporation of IIT Bombay's department oriented events like Yantriki, Chemsplash and Last Straw. Students from G H Raisoni College of Engineering got the Engineering Excellence Award for best design.

This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. This lecture series provides a thorough introduction to the cutting-edge research in deep learning appli... more

Artificial intelligence is very interesting topic of research for many modern scientists. The concept of machine intelligence is really fascinating. It gives human a power to design something that can live on its own. The AI technology has become really advanced and its only matter of time when the machines will be able to learn almost anything. The machine learning algorithms are already very smart, however the processing power has been a challenge in last decade.

This article is an effort to make you into a "semi-expert" in artificial intelligence, cognitive computing, deep learning and neural networks from scratch. Here I will share a few cool learning resources for these topics. These resources include documentaries, TED talks, online lecture videos, and books. There are several videos and online books included in this post to help you learn these concepts. These resources vary from introductory to advanced learning.