Couple weeks ago I develop AI model to automate trading processes. The performance is quite satisfying give it a try. It is open source everyone can use it and it's easy to setup but still there's problem that AI can't solve here's why: AI needs data for learning and not all exchange provide crypto or stock price historical data, Fortunately there's incredible python package called yfinance it's near to save us. Netrade is an AI image based model, it's learn from the chart pattern and candle stick pattern image like this But what exactly the problem is?, well the bitcoin data that i take using yfinance is from coinmarketcap not from our desire exchange. In my country there are couple of exchange like indodax, pintu, luno and etc in your country maybe there's binance or other exchange.

The growing usage of Artificial Intelligence (AI) in everyday computer systems is leading us down a path where the computer makes decisions and we, the humans, must live with the consequences. In any event, there's a lot of buzzes these days about how AI systems should be configured to provide explanations for anything they're doing. Explainable AI (XAI) is swiftly becoming a popular topic of discussion. People who use AI systems will most likely expect and perhaps demand that they be given an explanation. Given the rapidly increasing number of AI systems, there will be a large demand for a machine-produced explanation of what the AI has done or is doing.

However, researchers from Germany now claim that the standard used to automatically judge the realism of synthetic images is fatally flawed; and that the hundreds, even thousands of researchers around the world that rely on it to cut the cost of expensive human-based results evaluation may be heading down a blind alley. In order to demonstrate how the standard, Fréchet Inception Distance (FID), does not measure up to human standards for evaluating images, the researchers deployed their own GANs, optimized to FID (now a common metric). They found that FID is following its own obsessions, based on underlying code with a very different remit to that of image synthesis, and that it routinely fails to achieve a'human' standard of discernment: FID scores (lower is better) for images generated by various models using standard datasets and architectures. The researchers of the new paper pose the question'Would you agree with these rankings?'. In addition to its assertion that FID is not fit for its intended task, the paper further suggests that'obvious' remedies, such as switching out its internal engine for competing engines, will simply swap one set of biases for another.

Artificial intelligence is described as having machines do "smart" or "intelligent" matters on their very own barring human guidance. AI security entails leveraging AI to become conscious of and provides up cyber threats with much less human intervention than is usually predicted or wished with normal protection approaches. AI safety equipment are regularly used to pick out "good" versus "bad" with the aid of evaluating the behaviours of entities throughout surroundings to these in a comparable environment. This system allows the gadget to mechanically examine about and flag changes. Often known as unsupervised gaining knowledge of or "pattern of life" learning, this technique effects in giant numbers of false positives and negatives.

Artificial Intelligence (AI) is now being adopted for automation in various sectors -- from diagnosing medical conditions to regulating traffic and help drive vehicles. It is also used for running chatbots for customers to spotting signs of fraud in financial transactions. This new technology is also being adopted to read people's emotions and to "speak" to them as voice assistants. A majority of experts, however, are not sure whether AI should be so widely adopted without proper and adequate safeguards. They have expressed their concerns while talking to Pew Research.

Hyderabad, November 23, 2020 –– Analytics Insight conducted a survey "The Global Artificial Intelligence Trends 2020" to understand the global adoption of Artificial Intelligence (AI) amongst enterprises and recognize the business perceptions of AI across sectors. Analytics Insight reached out to 2,200 professionals online located in different geographic regions across a wide range of industries to explore different views toward AI and its current implications among enterprises. Receiving 256 responses for the survey, Analytics Insight articulated a detailed report, which can be indicative of the market as a whole. Out of the 256 respondents, 48.5% were working at small-scale companies with the company size of fewer than 100 employees. About 29.8% of the respondents were employed at companies which had total employees ranging from 100-1000, while 21.7% of respondents had a company size of over 1000 employees.

Facebook's Pytorch seems to have become a favoured choice among deep learning researchers and developers; however, TensorFlow is still believed to hold the top position for building machine learning models, and the debate continues. Pytorch is the second-fastest-growing open source project on Github and is famous for its advanced indexing, functions, integration support, API simplicity, and style. And, therefore, many AI and ML tech giants are either planning to switch to PyTorch or have already adopted it. Many companies have also been using Pytorch's advantages for research and production. OpenAI is the recent addition to the community of the tech giants that are using PyTorch; ending its TensorFlow usage. To contribute more towards the Pytorch community, OpenAI intends on introducing Deep RL educational resources on Pytorch.

Summary: Adoption of AI/ML by larger companies has more than doubled since last year according to these survey results from McKinsey and Stanford's Human-Centered AI Institute. This new data gives us a much better idea of which global regions and which industries are adopting which AI/ML techniques. We know we've entered the era of exploitation of AI/ML but the $64 Billion question is how far along the curve are we and who exactly has implemented and will implement? By the way, $64 Billion is a reasonable estimate of global market spend in roughly four or five years, about 6 times where we are today. And that investment should yield about $4 Trillion in business value in that same time frame according to Gartner.

With the strong demand for iPhone X, a Taiwanese supplier is predicting that the Cupertino giant's rivals will also adopt the 3D sensing technology Apple used on the 10th anniversary iPhone to their flagship handsets. The supplier is even predicting a rise in the demand for 3D sensing modules in the next 5 to 8 years. On Tuesday, DigiTimes reported that the chairman and CEO of digital camera and imaging solution supplier Altek, Alex Hsia, is expecting strong demand for 3D sensing modules in the following years now that Apple has introduced Face ID with its iPhone X flagship phone. "The structure-light 3D sensing technology adopted by Apple's iPhone X features high accuracy at short distances, and other first-tier smartphone vendors are expected to follow suit by launching 3D sensing-enabled devices," Hsia said. The CEO predicts that consumers will see more smartphones with 3D sensing technology starting next year.

Artificial intelligence has replaced big data this year as the most talked about new set of technologies. As with big data five years ago--behind the hype, the confusion generated by an ill-defined term, and the record funding by VC--we are starting to see emerging investments and practical applications where it matters most--in enterprises. A new report from Narrative Science, based on a survey of 235 business executives conducted by the National Business Research Institute (NBRI), sheds light on the state-of-AI in enterprises today and in the future: 38% of enterprises are already using AI technologies and 62% will use AI technologies by 2018. Keep in mind that "AI technologies" is a broad term that includes machine and deep learning, recommendation engines, predictive and prescriptive analytics, automated written reporting and communications, and voice recognition and response. Big data has spawned the current interest and increased investment in artificial intelligence.