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California allows companies to charge for autonomous car rides


One of the most common potential scenarios involving autonomous cars is using them as driverless taxis; both Uber and Lyft have made self-driving cars a big part of their future strategies. The possibility of hopping into a ride without a driver just got a little closer, at least in California -- as spotted by The Verge, California approved two new autonomous driving programs last week that let companies charge fares for autonomous rides. The two new programs are the "Drivered Autonomous Vehicle Deployment Program" and the "Driverless Autonomous Vehicle Deployment Program," both of which allow approved participants to offer "passenger service, shared rides, and accept monetary compensation for rides in autonomous vehicles." Naturally, interested companies need to get the necessary permits and show the California Public Utilities Commission (CPUC) that they're taking the proper safety measure. They'll need to get a AV Deployment Permit from California's DMV as well as one of two permits issued by CPUC.

Understanding Linear Regression


Let's say you're looking to buy a new PC from an online store (and you're most interested in how much RAM it has) and you see on their first page some PCs with 4GB at $100, then some with 16 GB at $1000. So, you estimate in your head that given the prices you saw so far, a PC with 8 GB RAM should be around $400. This will fit your budget and decide to buy one such PC with 8 GB RAM. This kind of estimations can happen almost automatically in your head without knowing it's called linear regression and without explicitly computing a regression equation in your head (in our case: y 75x – 200). So, what is linear regression? Linear regression is just the process of estimating an unknown quantity based on some known ones (this is the regression part) with the condition that the unknown quantity can be obtained from the known ones by using only 2 operations: scalar multiplication and addition (this is the linear part).

Tesla plans to offer machine-learning training as web service with its new 'Dojo' supercomputer - Electrek


Tesla plans to offer machine-learning training as a web service with its new'Dojo' supercomputer, according to new comments from CEO Elon Musk. Project "Dojo" was first announced by Musk at Tesla's Autonomy Day last year: We do have a major program at Tesla which we don't have enough time to talk about today called "Dojo." The goal of Dojo will be to be able to take in vast amounts of data and train at a video level and do unsupervised massive training of vast amounts of video with the Dojo program -- or Dojo computer. Dojo means "place of the Way" in Japanese and the term is often used for a place to practice meditation or martial arts. In this case, the Dojo supercomputer will be a place for Tesla to train its Full Self-Driving AI. Last month, Musk revealed that Tesla's Dojo supercomputer will be capable of an exaFLOP, one quintillion (1018) floating-point operations per second, or 1,000 petaFLOPS.

Demand Forecasting For Retail: A Deep Dive


I know for sure that human behavior could be predicted with data science and machine learning. Taking a look at human behavior from a sales data analysis perspective, we can get more valuable insights than from social surveys. In this article, I want to show how machine learning approaches can help with customer demand forecasting. Since I have experience in building forecasting models for retail field products, I'll use a retail business as an example. Moreover, considering uncertainties related to the COVID-19 pandemic, I'll also describe how to enhance forecasting accuracy.

The fulfilling Journey of Auria Kathi -- The AI Poet Artist living in the clouds


On 1st January 2019, we (Fabin Rasheed and I) had introduced to the world, a side project we've been working on for months. An artificial poet-artist, who doesn't physically exist in this world but writes a poem, draws an abstract art based on the poem and finally color the art based on emotion. We called "her" Auria Kathi -- an anagram for "AI Haiku Art". Auria has an artificial face along with her artificial poetry and art. Everything about Auria was built using artificial neural networks.

ROC Curve Explained in One Picture


With a ROC curve, you're trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve AuC). The ideal curve in the left image fills in 100%, which means that you're going to be able to distinguish between negative results and positive results 100% of the time (which is almost impossible in real life). The further you go to the right, the worse the detection. The ROC curve to the far right does a worse job than chance, mixing up the negatives and positives (which means you likely have an error in your setup).

Facebook using artificial intelligence to forecast COVID-19 spread in every U.S. county


State officials hope California's new 10 p.m. stay-at-home order will slow the spread of COVID-19, otherwise, another 10,000 San Diegans are projected to contract the virus in the next 10 days. That's according to a new county-by-county forecast from Facebook, which rolled out the prediction software last month. Facebook projects L.A. County will see the second-largest increase in cases in the country by November 30. San Diego County is projected to add the 15th most cases, reaching a total of 78,594 infections by Nov. 30. The two-week forecast was released before Governor Gavin Newsom announced enhanced restrictions.

Risks of artificial intelligence


When Deloitte's recent State of AI in the Enterprise study asked AI adopters about their organization's top adoption challenges, "managing AI-related risks" topped the list--tied with integration and data challenges, and on par with implementation concerns.1 And while worry is high, action to ameliorate risks is lagging: Fewer than one-third practice more than three AI risk management activities.2 And fewer than four in 10 adopters report that their organization is "fully prepared" for the range of AI risks that concern them. To investigate whether actively managing AI risks has any tangible benefit, we compared two groups of AI adopters that approach those risks differently: Risk Management Leaders (11%) undertake more than three AI risk management practices and align their AI risk management with their organization's broader risk management efforts, while Risk Management Dabblers (51%) undertake up to three AI risk management practices but are not aligning them with broader risk management efforts.3 The Leaders believe AI has greater strategic importance to their business: 40% see AI as "critically important" to their business today, versus only 18% of the Dabblers--and within two years, those numbers are expected to rise to 63% and 36%, respectively.

Exploiting AI: How Cybercriminals Misuse and Abuse AI and ML


Artificial intelligence (AI) is swiftly fueling the development of a more dynamic world. AI, a subfield of computer science that is interconnected with other disciplines, promises greater efficiency and higher levels of automation and autonomy. Simply put, it is a dual-use technology at the heart of the fourth industrial revolution. Together with machine learning (ML) -- a subfield of AI that analyzes large volumes of data to find patterns via algorithms -- enterprises, organizations, and governments are able to perform impressive feats that ultimately drive innovation and better business. The use of both AI and ML in business is rampant.

Pros & Cons Of Artificial Intelligence App Development


When you want to incorporate AI into your own mobile development procedure, it is important to learn how AI functions for different kinds of applications. Artificial Intelligence is a major portion of the mainstream these days as its innovation is integrated into almost all contemporary devices. Right from predictive analytics to chatbots, developers & organizations are constantly examining ground-breaking approaches for utilizing AI for delivering enhanced client services & reconsidering business procedures. The classifications of Artificial Intelligence in mobile application development companies are varied. It can be categorized into weak & strong.