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 success of deep learning over the last decade, particularly in computer vision, has depended greatly on large training data sets. Even though progress in this area boosted the performance of many tasks such as object detection, recognition, and segmentation, the main bottleneck for future improvement is more labeled data. Self-supervised learning is among the best alternatives for learning useful representations from the data. In this article, we will briefly review the self-supervised learning methods in the literature and discuss the findings of a recent self-supervised learning paper from ICLR 2020 . We may assume that most learning problems can be tackled by having clean labeling and more data obtained in an unsupervised way.
While the whole planet was frozen by the coronavirus pandemic, offline stores found they couldn't compete with even the smallest online stores when people's lifestyles were limited by their homes or neighborhoods. But those who have just started online sales this year will quickly find out what to do to sell efficiently on the internet. This is why the overall competition will rise. Wondering how you can gain a foothold at this moment? Take a look at modern technologies – artificial intelligence (AI), machine learning (ML), and big data analysis.
Artificial Intelligence (AI) is not the one that is borne by the overwhelming science fiction vision. In the near future, we will see almost every area of life in order to make our activities more effective and interactive. According to China's search engine, Baidu's top researcher, "Reliability of speech technology approaches the point we will only use and do not even think about." Andrew Ng says the best technology is often invisible, and speech recognition will disappear in the background as well. Baidu is currently working on more accurate speech recognition and more efficient sentence analysis, which expects sound technologies to be able to interact with multiple devices such as household appliances.
Convert the Xtrain and Ytrain data set into NumPy array because it will take for training the LSTM model.LSTM model has a 3-Dimensional data set [number of samples, time steps, features]. Therefore, we need to reshape the data from 2-Dimensional to 3-Dimensional. Below the code, snapshot illustrates a clear idea about reshaping the data set.Create the LSTM model which has two LSTM layers that contain fifty neurons also it has 2 Dense layers that one layer contains twenty-five neurons and the other has one neuron. In order to create a model that sequential input of the LSTM model which creates by using Keras library on DNN (Deep Neural Network). The compile LSTM model is using MSE (Mean Squared Error) for loss function and the optimizer to be the "adam".
Classical Analytics – Around ten years ago, the tools for analytics or the available resources were excel, SQL databases, and similar relatively simple ones when compared to the advanced ones that are available nowadays. The analytics also used to target things like reporting, customer classification, sales trend whether they are going up or down, etc.In this article we will discuss about Real Time Anomaly Detection. As time passed by the amount of data has got a revolutionary explosion with various factors like social media data, transaction records, sensor information, etc. in the past five years. With the increase of data, how data is stored has also changed. It used to be SQL databases the most and analytics used to happen for the same during the ideal time. The analytics also used to be serialized. Later, NoSQL databases started to replace the traditional SQL databases since the data size has become huge and the analysis also changed from serial analytics to parallel processing and distributed systems for quick results.
Take for example, the loan origination and loan servicing process in a financial institution. There are 5 key activities amongst several that if changed can fuel better productivity. So, if an AI engine is in place at activity 2, it can process customer data regarding financial history and propensity to pay etc. and flag potential defaulters or fraudsters. Similarly, AI-based chat bots can help improve customer service (activity 4) by either automating the transaction completely or offering sentiment-analysis based insights to agents for better customer experience(see Figure 1). Bringing technology in these areas will improve productivity and reduce cost and effort, validating investment.
The biggest advantage, obviously, is the potential to meet thousands of eligible singles who you likely wouldn't have known existed otherwise. But whether those singles use their profile regularly or are even on it for the right reasons is another question -- thus, the terrifying edge that can cause singles genuinely searching for the real thing to shy away from such a valuable tool. When the dating pool is so deep, it's important to narrow down your options to dating sites that are most likely to attract a very specific type of person and introduce you to people who have the same intentions that you do. Whether unspoken or not, eharmony and Elitesingles are two websites for serious relationships that make those kind of definitions clear. After deciding that online dating is your best shot at meeting someone who's in it for the long haul, eharmony probably immediately came to mind.
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.