Face-to-face interaction with peers and professors combined with longer duration programs help you learn faster. If you could learn all this over a few weekends then everybody would be just doing that. A full-time, classroom based training that's rigorous, practical and gets you placed is always a better bet. It's better to learn this from experienced business practitioners in AI & ML than from those who just teach. Practice is what you need to get forward in this industry.

When it comes to digital transformation, humans--believe it or not--play an integral role. In fact, companies that make strong use of the combined human/machine workforce have a far greater chance of success in digital transformation. Accenture calls these combined people/bot workspaces "future systems" – systems that seamlessly integrate humans and robots to create business goals that are limitless, agile, and "radically human." I consider the companies that harness the power of humans and machines will be the ultimate winners of the future of work. The good news: these systems are already happening.

With the growing scare of the deadly coronavirus, companies in China are pushing hard to limit its spread. In one such effort, the country's leading search engine Baidu has open-sourced an AI model to detect people not wearing face masks. As coronavirus can spread through close contact with an infected person via their coughs, sneezes, or respiratory droplets, China has made it mandatory to wear face masks in several regions. People are instructed to wear masks in public places such as restaurants, shopping malls, and public transport. It's quite hard for authorities to catch people not wearing masks in large crowds.

Existing intellectual property laws don't allow AI systems to be recognized as inventors, which threatens the integrity of the patent system and the potential to develop life-changing innovations. Current legislation only allows humans to be recognized as inventors, which could make AI-generated innovations unpatentable. This would deprive the owners of the AI of the legal protections they need for the inventions that their systems create. The Artificial Inventor Project team has been testing the limitations of these rules by filing patent applications that designate a machine as the inventor-- the first time that an AI's role as an inventor had ever been disclosed in a patent application. They made the applications on behalf of Dr Stephen Thaler, the creator of a system called DABUS, which was listed as the inventor of a food container that robots can easily grasp and a flashing warning light designed to attract attention during emergencies.

The Cabinet on Tuesday approved a bill to support companies to develop secure 5G mobile networks and drone technologies amid growing alarm among Tokyo policy-makers over the increasing influence of China's 5G technology. The bill will give companies which develop such technologies access to low-interest rate loans from government-affiliated financial institutions if their plans fulfill standards on cyber security. Companies that adopt 5G technologies can also get tax incentives if they meet standards set by the government, according to the bill. The government will submit the bill to the parliament and aims to bring it to effect around summer. The United States has been waging a campaign against Huawei Technologies Co, which Washington has warned could spy on customers for Beijing.

The Dubai Silicon Oasis (DSO) in partnership with Intel has announced the launch of a new phase of the Intel Innovation Center in the integrated free zone technology park. The new phase will be hosted by Dubai Technology Entrepreneur Campus (Dtec), DSOA's wholly owned tech incubation center. Moreover, the Intel Innovation Center's new phase will directly be aligned with "Project Mustakbal", an Intel initiative that seeks to accelerate the Middle East's digital transformation. The Centre set to become a hub for future technological development in the region that will feature artificial intelligence (AI), Blockchain, Video analytics and Autonomous Driving. Muammar Al Katheeri, Executive Vice President of Engineering and Smart City at DSOA, said in a statement, "Four years ago, we launched with Intel the region's first Internet of Things (IoT) ignition lab that has already added significant value to tech start-ups and entrepreneurs in the UAE. Today we celebrate our partnership with Intel as we step forward together into a new milestone through the inauguration of the Intel Innovation Center that has found an ideal home at DSO. With its dynamic mix of business partners and boasting an environment that fosters the entrepreneurial spirit, DSO continues to push the boundaries of technological innovation."

With the help of one example, we show how a dramatic reduction in RNA sequencing depth has little to no impact on the performance of machine learning-based linear Cox models that predict disease outcome based on tumor gene expression. Since this analysis is peformed in R, if you have not installed it yet, you can follow the intructions in https://cran.r-project.org/. In case R is installed, it needs to be version 3.6.1 or higher for this example to work. The following code can help determine if R needs to be updated. In this example, we will use adrenocortical carcinoma (ACC) to demonstrate how a drastic reduction in RNA-seq depth still gives enough information to predict the relative risk of adverse outcome of disease.

All measurements, including biological measurements, contain a tradeoff between precision and throughput. In sequencing-based measurements like mRNA-sequencing (mRNA-seq), precision is determined largely by the sequencing depth applied to individual samples. At high sequencing depth, mRNA-seq can detect subtle changes in gene expression including the expression of rare splice variants or quantitative modulations in transcript abundance. However, such precision comes at a cost, and sequencing transcripts from 10,000 single cells at deep sequencing coverage (106 reads per cell) currently requires 2 weeks of sequencing on an Illumina HiSeq 4000. Not all biological questions require such extreme technical sensitivity. For example, a catalog of human cell types and the transcriptional programs that define them can potentially be generated by querying the general transcriptional state of single cells ( Trapnell, 2015 Defining cell types and states with single-cell genomics.

Most product-development tasks are complex optimization problems. Design teams approach them iteratively, refining an initial best guess through rounds of engineering analysis, interpretation, and refinement. But each such iteration takes time and money, and teams may achieve only a handful of iterations within the development timeline. Because teams rarely have the opportunity to explore alternative solutions that depart significantly from their base-case assumptions, too often the final design is suboptimal. Today's technology offers an alternative.

"Generalization" is an AI buzzword these days for good reason: most scientists would love to see the models they're training in simulations and video game environments evolve and expand to take on meaningful real-world challenges -- for example in safety, conservation, medicine, etc. One concerned research area is deep reinforcement learning (DRL), which implements deep learning architectures with reinforcement learning algorithms to enable AI agents to learn the best actions possible to attain their goals in virtual environments. DRL has been widely applied in games and robotics. Such DRL agents have an impressive track record on Starcraft II and Dota-2. But because they were trained in fixed environments, studies suggest DRL agents can fail to generalize to even slight variations of their training environments. In a new paper, researchers from the New York University and Modl.ai, a company applying machine learning to game developing, suggest that simple spacial processing methods such as rotation, translation and cropping could help increase model generality.