Cloudera has launched Applied ML Prototypes, complete machine learning projects for use cases that give data scientists a running start on development. The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Applied ML Prototypes (AMPs) are available within Cloudera Machine Learning. By taking care of a lot of the coding and workflow grunt work, data scientists can focus on developing for the enterprise use case and iterating. Cloudera plans on adding dozens of AMP use cases that will accelerate the use of emerging machine learning.

The decision tree Algorithm belongs to the family of supervised machine learning algorithms. It can be used for both a classification problem as well as for regression problem. The goal of this algorithm is to create a model that predicts the value of a target variable, for which the decision tree uses the tree representation to solve the problem in which the leaf node corresponds to a class label and attributes are represented on the internal node of the tree. It will split our data into two branches High and Normal based on cholesterol, as you can see in the above figure. Let's suppose our new patient has high cholesterol by the above split of our data we cannot say whether Drug B or Drug A will be suitable for the patient.

Heading into 2021, the future of artificial intelligence (AI) in technology and cybersecurity will only continue to evolve as more organizations adopt new and innovative techniques. According to one recent survey, two-thirds of organizations are already using the intelligent technology for cybersecurity purposes. Using these tools allows for companies to be more prepared for the innovative attacks that cybercriminals continue to develop – also using AI technologies. For example, just last year, criminals employed AI-based software to replicate a CEO's voice to command a cash transfer of €220,000 (approximately $243,000). For businesses looking to implement more AI into their security stack in 2021, it's important to follow these six steps to ensure the effective use of AI – without compromising security anywhere else down the line.

The COVID-19 pandemic disrupted labor markets globally during 2020. The short-term consequences were sudden and often severe: Millions of people were furloughed or lost jobs, and others rapidly adjusted to working from home as offices closed. Many other workers were deemed essential and continued to work in hospitals and grocery stores, on garbage trucks and in warehouses, yet under new protocols to reduce the spread of the novel coronavirus. This report on the future of work after COVID-19 is the first of three MGI reports that examine aspects of the postpandemic economy. The others look at the pandemic's long-term influence on consumption and the potential for a broad recovery led by enhanced productivity and innovation.

Algorithmic fairness is a motif that plays throughout our podcast series: as we look to AI to help us make consequential decisions involving people, guests have stressed the risks that the automated systems that we build will encode past injustices and that these decisions may be too opaque. In episode twelve of the Intel on AI podcast, Intel AI Tech Evangelist and host Abigail Hing Wen talks with Alice Xiang, then Head of Fairness, Transparency, and Accountability Research at the Partnership on AI--a nonprofit in Silicon Valley founded by Amazon, Apple, Facebook, Google, IBM, Intel and other partners. With a background that includes both law and statistics, Alice's research has focused on the intersection of AI and the law. "A lot of the benefit of algorithmic systems, if used well, would be to help us detect problems rather than to help us automate decisions." Algorithmic fairness is the study of how algorithms might systemically perform better or worse for certain groups of people and the ways in which historical biases or other systemic inequities might be perpetuated by AI.

The NYPD deployed its new robotic dog to a home invasion crime scene in the Bronx on Tuesday morning, new video shows. Video shows the blue and black, four-legged "Digidog" trotting along the sidewalk, joined by its handlers. A spokeswoman for the NYPD said the 70-pound robot is in its test phase and is equipped with lights and cameras to allows cops to see "its surroundings in real-time." It also comes with two-way communication, the spokewoman added. Cops confirmed responding to that address for an ongoing investigation and finding no one there.

Psychologists have long found that people behave differently than when they learn of peers' actions. A new study by computer scientists found that when individuals in an experiment about autonomous vehicles were informed that their peers were more likely to sacrifice their own safety to program their vehicle hit a wall rather than hit pedestrians who were at risk, the percentage of individuals willing to sacrifice their own safety increased by approximately two-thirds. As computer scientists train machines to act as people's agents in all sorts of situations, the study's authors indicate that the social component of decision-making is often overlooked. This could be of great consequence, note the paper's authors who show that the trolly problem –long shown to be the scenario moral psychologists turn to--is problematic. The problem, the authors indicate, fails to show the complexity of how humans make decisions.

Embry-Riddle researchers are working on a solution to a significant safety problem involving semi-autonomous vehicles after crashes occurred when the vehicles did not detect firetrucks or police cars in the roadway. Partnering with the Emergency Responder Safety Institute and a private company called HAAS Alerts, Scott Parr, assistant professor of Civil Engineering, and Patrick Currier, associate professor and associate chair of the Mechanical Engineering Department, plan to employ digital signals to alert the autonomous vehicles (AVs) of the presence of emergency response vehicles. The plan would effectively employ emergency vehicle location signals -- now provided by HAAS Alerts to route mapping applications whenever a geolocation device mounted to the lighting bar of emergency vehicles is activated -- and extend them to also communicate with AVs. "We're trying to demonstrate that this technology does work and that it can be a solution to the problem," said Parr, adding that a response system to the alerts will be manually programmed into AVs owned by Embry-Riddle as a demonstration. The system would enact an automatic protocol to slow or stop the AV depending on how close it was to the emergency vehicle.

It was last summer that I wrote about the Japanese computing giant'Fugaku' surpassing the American reigning champion Summit to become the fastest supercomputer in the World. Since then, Fugaku has solidified its position at the top spot -- according to the 56th edition of the TOP500 list published in Nov. 2020, its capacity has increased from 7,299,072 cores to 7,630,848 cores, posting a new world record 442 petaflops result on HPL. The most powerful supercomputer by RIKEN Center for Computational Science & Fujitsu has now been engaged in developing a real-world prediction tool. In a multinational collaborative endeavor, The International Research Institute of Disaster Science at Tohoku University, the Earthquake Research Institute at the University of Tokyo, and Fujitsu Laboratories have come together to develop an AI model that will be able to predict tsunami flooding in coastal areas in near real-time. This could be a real handy tool for disaster management teams.

Renesas has announced the expansion of its RZ/G2 general-purpose 64-bit microprocessors, with improved artificial intelligence (AI) processing. The company has added three entry level microprocessor models built around the Arm Cortex-A55 core. Renesas adds that the seven RZ/G2 microprocessors provide scalability from entry-level to high-end design. The RZ/G2Lx microprocessors' Arm Cortex-A55 CPU core delivers approximately 20 per cent improved processing performance compared with the previous Cortex-A53 core. It also provides approximately six times faster essential processing for AI applications, says the company.