If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
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."
The tech talent shortage has CIOs scrambling to ensure that employees brush up on the latest skills and technologies that facilitate business agility. A lack of skills and resources has been either the No. 1 or the No. 2 obstacle to achieving organizational objectives, according to the past four annual Gartner CIO surveys. "Strategic workforce management has changed drastically in the past 10 years, and the complexity is only increasing as the labor market tightens," says Gartner analyst Alex Johnston. "Organizations are competing for skill sets that were previously unheard of but are now in extremely high demand." With digital business moving faster than organizations can keep up, CIOs need to do something different to get a different result.
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.
Deepfakes leverage powerful techniques from machine learning and artificial intelligence to generate visual and audio content with a such a high degree of realism that it has enormous potential to deceive. This article in Medium explores efforts into research and development into creating countermeasures to such bogus content. Within recent months, a number of mitigation mechanisms have been proposed and cited with the use of Neural Networks and Artificial Intelligence being at the heart of them. From this, we can distinguish that a proposal for technologies that can automatically detect and assess the integrity of visual media is therefore indispensable and in great need if we wish to fight back against adversarial attacks. (Nguyen, 2019)
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.
The call center has always been an important way for banks to connect with customers. But it won't be long before call centers without artificial intelligence (AI) will be unable to make those human connections. I'm reminded of a meeting that I and one of my colleagues recently had with a bank CEO. My colleague remarked that he and his wife had been a customer of the bank for 25 years. Yet, in a recent interaction with a customer service representative (CSR) at the bank, the representative was unaware that my colleague's daughter had recently turned 18 and begun her financial life as an adult.
If you want to master Python programming language then you can't skip projects in Python. After publishing 4 advanced python projects, DataFlair today came with another one that is the Breast Cancer Classification project in Python. To crack your next Python Interview, practice these projects thoroughly and if you face any confusion, do comment, DataFlair is always ready to help you. An intensive approach to Machine Learning, Deep Learning is inspired by the workings of the human brain and its biological neural networks. Architectures as deep neural networks, recurrent neural networks, convolutional neural networks, and deep belief networks are made of multiple layers for the data to pass through before finally producing the output.