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"Big data" - this is just like its name. "Big data" refers to huge data sets brought by new technologies such as the "Internet" and genomics. These data sets are extremely large and complex, so that we cannot analyze them using traditional application software. We need to develop new application software to analyze all these "big data". In a recent article, we analyzed the top 5 data storage companies by revenue. Only one of those companies is a viable investment target. Perhaps the biggest player in the field today is Palantir Technologies, a privately-held company with $25 billion in annual revenue that helps interpret all these "big data." It's one thing to store this data, and it's quite another to analyze this data because 80% of all data today is not structured data such as news articles, research reports, and corporate data. This leads to the next term.
“Deep learningâ€â€”This area essentially means that we can teach computers to use all unstructured big data and start using various algorithms to interpret data, such as “artificial neural networks that simulate the way the brain works.†". Deep learning uses algorithms to find all the complex relationships in "big data", and then we further refine those algorithms to make them more perfect. The gradual learning of computer functions based on experience is the natural way of working of the human brain, sometimes called "cognitive computing." We may all have heard of IBM's cognitive computing platform called Watson, which uses deep learning techniques to convert speech into text and convert text into speech. Although there is currently no professional "deep learning" stock, there are several start-up companies that are exploring the application of deep learning in many industries. Deep learning or cognitive computing is a form of artificial intelligence, which leads to the next term.
Artificial intelligence - This term refers to the fact that computers begin to process data and infer complex relationships, just as humans do. How can we measure this? The most popular method is called the Turing Test, but some researchers dismiss it as thinking that only amateurs will be interested in it. Although IBM is the main owner of artificial intelligence patents (with more than 500 patents), there are several other start-up companies in the field of artificial intelligence, such as the company we introduced earlier called Vicarious. In fact, everyone is optimistic about this company. The software code developed by Vicarious can imitate the human brain while using relatively small amounts of data and computing power. Although micro-computing capabilities are reasonable today, if we can do quantitative sub-calculations, this may not be a problem.
Quantum Computing - This term means that we use magical quantum physics to create a computer that is much more powerful than any existing computer. We may start talking about "quantum entanglement" and the need to freeze things to absolute zero, but ordinary people don't care about any of these technical details. What kind of potential quantum computing has, what stage are we currently in? Recently, Google announced that it used D-Wave 2 to solve an optimization problem with 1000 variables, which is 100 million times faster than traditional computers. Image-wise, the tasks that D-Wave 2 can handle in one second require traditional computers to take 10,000 years to process. How will you invest in quantum computing? At present, there are not many vendors in this field.
All these technologies are expected to usher in rapid growth in the coming years. How can retail investors make money in this area? The name IBM is constantly mentioned, so is IBM a suitable investment target in these fields? With revenues of US$92 billion in 2014, the “big data†and Watson’s current contributions have had a negligible impact on overall company revenue. Having said that, we still like IBM's 3.8% yield, which is guaranteed by a payout ratio of less than 50%.
Although the most promising companies are still private companies, there will be exit events and initial public offerings (IPOs) in the future that will benefit investors. The next stop for retail investors to look at is the future of technology companies IPO in these areas. According to data analysis company CB Insights, there are two "big data" startup companies that may have an IPO in 2016. In fact, CB Insights has listed more than 530 technology startups that may be IPOs in 2016, many of which are related to "big data."
1. Apply to pig nursery bed, sow farrowing crate, water-saving feed to prevent pollution, keep piggery clean.
2. Made of 304 stainless steel two holes are fixed.To prevent water overflow.
3. Effectively prevent metal Drinking Bowl cut snout to avoid trauma infection.
4. The pig water fountain obviously save more than 30% of the water, reduce the sewage farm.
5. Can be put on some medicine when drinking water, to achieve the effect of saving medicine.
Key technologies such as cloud computing artificial intelligence
"Cloud computing" - This concept means that you can rent applications as needed and then access them over the Internet instead of buying applications and then installing applications on your computer. That's it. This is cloud computing. You may have heard of the term "software as a service" ("SaaS" for short). This is actually one thing. It refers to a kind of centralized hosting software obtained through subscription services. For investors, this area is on the track, and companies like Salesforce.com, a customer relationship management (CRM) SaaS provider, have a 10-year return of over 780%. However, "Cloud computing" and "SaaS" have become old news for investors seeking the next big deal in the computer industry.