I was reading this article on the New Yorker from 2017 on the use of CNNs in identifying cancer by image analysis. The CNN was trained using a data set of 130k images and performed better than experts. What I don't understand is the author's contention that this neural network performed better when it was pre-trained on data having nothing to do with the cancer lesion problem. Here is the author's quote: "There's one rather profound thing about the network that wasn't fully emphasized in the paper," Thrun told me. In the first iteration of the study, he and the team had started with a totally naïve neural network.

Some people associate AI with science fiction robots, but in reality, the technology streamlines and improves many day-to-day processes, allowing us to accomplish more in less time. Today, AI plays a role in many aspects of our daily lives, from commuting to shopping to browsing the web. AI software development is a massive market, and the technology is improving all the time. Experts predict that the AI social media market will grow from $633 million in 2018 to $2.1 billion in 2023. As both artificial intelligence and social media become more prevalent in people's lives, the intersection of the two will also become more popular.

BlackLine, Inc. (Nasdaq: BL) announced today that Sophia Velastegui of Microsoft has joined the accounting automation software leader's board of directors, effective March 16th, 2020, bringing the number of female directors on BlackLine's board to three. A widely recognized technology industry leader and artificial intelligence (AI) expert, Ms. Velastegui will draw on her more than 20 years of experience at global technology trailblazers Microsoft, Google and Apple to help BlackLine maintain its leadership position and guide the company through its next phase of growth. "Sophia has an incredible technology background and is recognized as one of the foremost engineers in the field," said BlackLine CEO Therese Tucker. "She'll bring that experience and knowledge to our board and will help ensure that BlackLine utilizes the latest AI, machine learning and other predictive and emerging technologies to continue to innovate and serve our customers. And, she shares my passion, advocating for diversity in technology and leadership."

We have a PhD level or Advanced courses thread in the sidebar but it's three year old now. There were two other 7-8 month old threads (1, 2) but they don't have many quality responses either. So, can we have a new one here? To reiterate - CS231n, CS229, ones from Udemy etc are not advanced. Advanced ML/DL/RL, attempts at building theory of DL, optimization theory, advanced applications etc are some examples of what I believe should belong here, much like the original sidebar post.

The demand for people with knowledge and skills in artificial intelligence (AI) and machine learning (ML) hugely outstrips the supply.

This article has been contributed by Lin Ma, Software Engineer and KVM Virtualization Specialist at SUSE. With this article, I would like to introduce you to my SUSE Hackweek 19 project. If you worked on similar projects or topics, or if you would like to exchange experiences, please feel free to reach out to me. As a Do-it-Yourself (DIY) enthusiast, I decided to have some fun during Hackweek with music and machine learning based on SUSE Linux Enterprise Server. Or, if you will, you could also think of my project as an Internet of Things (IoT) attempt based on SUSE products.

This is according to the Westpac First Job Report, which found Australian teens are landing their first job at an average age of 15.4 years, compared to 16.3 years for their parents. While seven out of 10 young people surveyed prioritised money and financial independence when looking for their first job, more than two thirds lacked the confidence to open a bank account to be paid into. Hospitality is the most common industry most Australians will work in for their first job.Source:istock Job administration, such as setting up a tax file number and superannuation, was another area where teens (57 per cent) felt unsure and almost half (44 per cent) were too afraid to ask in an interview how much a job paid. To provide guidance to young job seekers, Westpac launched Wendy, Australia's first digital job coach. Meet Wendy, Westpac's first digital job coach who listens and responds in real time.Source:Supplied Using Wendy is similar to conducting a FaceTime call as she can listen, speak and recognise human emotions in real-time.

This week saw the return, for a third season, of the critically acclaimed HBO series Westworld. WW's central premise in its first 2 seasons was a theme park, sometime in the near future, populated by highly realistic robots or'hosts'. Human guests can pay exorbitant sums to interact with these robots, in a huge range of ways. In the'western' themed area – after which the show is named – guests can choose to be white-hatted heroes or black-hatted villains. The good guys get to be brave, chivalrous, honourable and generally decent.

The automatic identification of propaganda has gained significance in recent years due to technological and social changes in the way news is generated and consumed. That this task can be addressed effectively using BERT, a powerful new architecture which can be fine-tuned for text classification tasks, is not surprising. However, propaganda detection, like other tasks that deal with news documents and other forms of decontextualized social communication (e.g. sentiment analysis), inherently deals with data whose categories are simultaneously imbalanced and dissimilar. We show that BERT, while capable of handling imbalanced classes with no additional data augmentation, does not generalise well when the training and test data are sufficiently dissimilar (as is often the case with news sources, whose topics evolve over time). We show how to address this problem by providing a statistical measure of similarity between datasets and a method of incorporating cost-weighting into BERT when the training and test sets are dissimilar. We test these methods on the Propaganda Techniques Corpus (PTC) and achieve the second-highest score on sentence-level propaganda classification.

High-dimensional autoregressive point processes model how current events trigger or inhibit future events, such as activity by one member of a social network can affect the future activity of his or her neighbors. While past work has focused on estimating the underlying network structure based solely on the times at which events occur on each node of the network, this paper examines the more nuanced problem of estimating context-dependent networks that reflect how features associated with an event (such as the content of a social media post) modulate the strength of influences among nodes. Specifically, we leverage ideas from compositional time series and regularization methods in machine learning to conduct network estimation for high-dimensional marked point processes. Two models and corresponding estimators are considered in detail: an autoregressive multinomial model suited to categorical marks and a logistic-normal model suited to marks with mixed membership in different categories. Importantly, the logistic-normal model leads to a convex negative log-likelihood objective and captures dependence across categories. We provide theoretical guarantees for both estimators, which we validate by simulations and a synthetic data-generating model. We further validate our methods through two real data examples and demonstrate the advantages and disadvantages of both approaches.