It appears we may not be ready to turn over important news curation duties to AI-driven editors, according to an opinion piece in Analytics India. Case in point: Trusting AI to get the story right resulted in a major gaffe at AI-driven MSN News last week, observes Analytics India writer Ram Sugar. The problem: MSN's AI ran the wrong photo along with a piece on racism, which it curated from another online news source. That triggered charges from the misidentified source -- Jade Thirwall -- that the AI software itself was plagued by racist programming. Observes Sugar: "Having a completely automated information curator cannot be justified -- unless some organization wants to hide behind the veil of AI by shifting the blame to a non-human entity."

These aren't sentient machines, but they are self-learning.

They are designed specifically to handle the very large machine-learning models that are being used for breakthroughs in image processing, natural …

Mari Anne Bayliss, senior director of solution management at CyberSource, told Karen Webster that simply relying on machine learning as a weapon …

The COVID-19 pandemic is an incredibly complex and rapidly evolving global public health emergency. Facebook is committed to preventing the spread of false and misleading information on our platforms. Misinformation about the disease can evolve as rapidly as the headlines in the news and can be hard to distinguish from legitimate reporting. The same piece of misinformation can appear in slightly different forms, such as as an image modified with a few pixels cropped or augmented with a filter. And these variations can be unintentional or the result of someone's deliberate attempt to avoid detection.

Information technology has spread widely, and extraordinarily large amounts of data have been made accessible to users, which has made it challenging to select data that are in accordance with user needs. For the resolution of the above issue, recommender systems have emerged, which much help users go through the process of decision-making and selecting relevant data. A recommender system predicts users behavior to be capable of detecting their interests and needs, and it often uses the classification technique for this purpose. It may not be sufficiently accurate to employ individual classification, where not all cases can be examined, which makes the method inappropriate to specific problems. In this research, group classification and the ensemble learning technique were used for increasing prediction accuracy in recommender systems. Another issue that is raised here concerns user analysis. Given the large size of the data and a large number of users, the process of user needs analysis and prediction (using a graph in most cases, representing the relations between users and their selected items) is complicated and cumbersome in recommender systems. Graph embedding was also proposed for resolution of this issue, where all or part of user behavior can be simulated through the generation of several vectors, resolving the problem of user behavior analysis to a large extent while maintaining high efficiency. In this research, individuals most similar to the target user were classified using ensemble learning, fuzzy rules, and the decision tree, and relevant recommendations were then made to each user with a heterogeneous knowledge graph and embedding vectors. This study was performed on the MovieLens datasets, and the obtained results indicated the high efficiency of the presented method.

I made a website that uses GPT3 to generate recommendations from a natural language query:...

Though parts of the world have succeeded in suppressing the coronavirus and are now opening up, it will be some time before we can start traveling to conferences again. I was supposed to attend two meetings this spring and then the Telluride Neuromorphic Engineering Workshop this summer. I enjoy poring through the literature, but was looking forward to hearing from the researchers themselves. So I decided to console myself by putting together a list of (mostly) recent technical neuromorphic video talks available online and have shared these with the neuromorphic community (and now with you). I find conference presentations a much better way into new subject matter than papers: you get a context, explanation, and overview without being bogged down with technical details.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. Kristen Doute said she is doing some soul-searching after being fired from "Vanderpump Rules" along with castmate Stassi Schroeder for past racially insensitive actions involving former Black cast member Faith Stowers. The 37-year-old spoke about how she's changing and growing as a person on the "Hollywood Raw" podcast with Dax Holt and Adam Glyn. "It was definitely none of my business to take anything to social media [and] essentially send a mob out to this person. It was really just not my place to go there," she said.

I was trying my hand on Optical Character Recognition on newspaper images when I realised that most documents have sections and text is not necessarily across the entire horizontal space of the page. Even though Tesseract was able to recognise the text it was jumbled up. To fix this the model should be able to identify sections on the document and draw a bounding box around it an perform OCR. It was this moment when applying Yolo Object detection on such images came into mind. YOLOv3 is extremely fast and accurate.