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) …
At a time like this, the banking sector is trying its hand, leg and even head to give a head-start to the AI developments. The financial services industry is appealing to enter AI market to avail the luxury of accurate data and investment. The development assists banks with better customer service, fraud detection, reduction of managing cost and easy decision-making through AI analysis. Customers have expectations that can't be turned down. Expectations to get work done faster and with zero error. The only by-standing solution is the utilisation of AI in the everyday banking sector.
Based out of Singapore, Gero develops new drugs for ageing and other complicated disorders using its proprietary developed artificial intelligence (AI) platform. Recently, the company has secured $2.2 million (€1.9 million) in Series A funding, bringing the total capital raised since Gero's founding to over $7.5 million (€6.4 million). Gero's founder Peter Fedichev, said, "We are happy with the recognition and support from these strategic investors who themselves are acknowledged leaders in the fields of AI and biotechnology. This will help us attain the necessary knowledge at the junction of biological sciences and AI/ML technologies that is necessary for the radical acceleration of drug discovery battling the toughest medical challenges of the 21st century. We hope that the technology will soon lead to a meaningful healthspan extension and quality of life improvements " The round was led by Bulba Ventures with participation from previous investors and serial entrepreneurs in the fields of pharmaceuticals, IT, and AI.
Robotic machinery that is being used in industries to assemble airplanes and smart phones are vulnerable to cyber attacks say security experts from Trend Micro Inc. And the researchers argue that most of such machinery is susceptible to hacking activities like data steal and remotely altering the movement of robots. Trend Micro's report titled "Robot Automation" says that industrial environments having robotic machinery are exposed to serious consequences like machinery failure, physical damage to operators and sometimes injuries and life loss to them. Technically, robots run with the help of systems driven by operating systems and some vulnerability in them could make cyber criminals to induce malicious codes into them and program them remotely to run as per their likes. For instance, they found App based software produced by ABB LTD from Switzerland to be exhibiting certain flaws that when explored by hackers could bring operational troubles to industrial firms- especially those related to automobile sector.
For any large-scale computer vision application, one of the critical criteria to success is the quality and quantity of the training dataset required to train the relevant machine learning model. Open-source datasets such as ImageNet are sufficient to train machine learning models for computer vision applications that do not require high accuracy or are not too complicated, But for more complex use cases, obtaining a large amount of high-quality training data can be quite challenging, such as autonomous driving, safety monitoring systems, medical image diagnosis and more. In this article, we take a look at how to quickly create (including collection, labelling, and quality inspection) high-quality training data sets for various computer vision scenarios. Different types of machine learning modelling methods may use different types of training data. The main difference in data type is the degree to which it is marked.
Anchor texts are hyperlinked, clickable words in the content. Working on anchor text optimization for both internal and external content can help your site get a better ranking on search engines. In 2011 and earlier, companies gained good rankings by using keyword-rich links as anchor texts. In 2012, Google released the Penguin update, which caused businesses that had exact matching keywords to suffer major slides in Google rankings. Businesses started considering the use of anchor text optimization strategies to recover the lost ground.
Robotic Process Automation (RPA) and Artificial Intelligence (AI) have garnered a lot of hype lately driving never-before-seen productivity across businesses! The global RPA market is showing an interesting growth and is poised to reach US$10.7 billion by 2027, and its bigger cousin the AI market is forecast to reach a monumental US$390.9 billion by 2025. However, despite the many conversations these promising disruptive technologies have sparked recently, there is still a lot of confusion that looms over the crucial differentiating factors, USPs, and how both these technologies increasingly to work in an amazing tandem. Modern tech-powered enterprises integrate both simple processes and those rich in complex decision making, needing a set of complementary technologies to handle the full range of their workflows. RPA and AI combine to form a winning team, Robotic Process Automation systems have a clear, step-by-step flow, while Artificial Intelligence powers to augment and improve human-inspired machine decision making a complex mix-a-mix.
Radiant Earth Foundation has released "LandCoverNet," a human-labelled global land cover classification training dataset. This release contains data across Africa, which accounts for 1/5 of the global dataset. Available for download on Radiant MLHub, the open geospatial library, LandCoverNet will enable accurate and regular land cover mapping for timely insights into natural and anthropogenic impacts on the Earth. Global land cover maps derived from Earth observations are not new, but the influx of open-access high spatial resolution Earth observations, such as that from the European Space Agency's Sentinel missions, coupled with improved computer power, encouraged the development of advanced algorithms. Machine learning models applied to high resolution remotely sensed imagery can classify land cover classes more accurately and faster, given the availability of high-quality training data.
Richard Harmon, Managing Director of Financial Services at Cloudera, discusses the importance of relevant machine learning models in today's age, and how the financial sector can prepare for future changes. The past six months have been turbulent. Business disruptions and closures are happening at an unprecedented scale and impacting the economy in a profound way. In the financial services sector, S&P Global estimates that this year could quadruple UK bank credit losses. The economic uncertainty in the UK is heightened by Brexit, which will see the UK leave the European Union in 2021.