In the past decade or so, leading investigators have begun to incorporate social networks into their models, trying to identify and analyze patterns of individual behavior that amplify or mute potential pandemics.

Here’s an interesting article from the NYT.

Those findings, in turn, inform policy recommendations. When does it make sense to shut down schools or workplaces? When will closing a border make a difference, and when won’t it? World health officials consult with social network modelers on a near daily basis, and Dr. Vespignani’s lab is part of one of several consortiums being consulted in the crucial and perhaps disruptive decisions coming in the next few weeks. On Friday, in an analysis posted by the journal Science, the group estimated that China’s travel ban on Wuhan delayed the growth of the epidemic by only a few days in mainland China and by two to three weeks elsewhere. “Moving forward we expect that travel restrictions to COVID-19 affected areas will have modest effects,” the team concluded.

The following is a guest post by Molly Crockett.

Molly Crockett is a technical writer who works for Big Assignments and OX Essays. She writes primarily about digital machine learning and artificial intelligence. She is passionate about researching ways that AI is poised to revolutionize different industries. Ellie also writes and teaches at Elite Assignment Help.


There are a lot of opportunities to bring artificial intelligence (AI) to the table with cryptocurrency trading, since trading has already made the switch to digital, and AI is revolutionizing all industries. However, with that being said, combining an industry and AI just for the sake of it might not always work as smoothly as we all think it would just based on the prior success of AI. In this article, we’ll explore if AI and trading is possible or if it’s just a pipe dream, and if it is possible if it’s even a good idea.

1. Most of trading is already done through computer algorithms.

It’s more and more difficult to compete with traders that have only some human training skills, since computers and computer training is getting so popular. To compete, it’s absolutely essential to make fast decisions. According to the latest stats, computers now generate between 50 and 70 percent of trading in all equity markets, 60 percent of future and over 50 percent of treasuries. This type of machine learning and AI is used more and more frequently and to great success to analyze the data, provide investment guidance, and trade securities.

2. AI is necessary to be able to analyze all the digital data.

There is just so much data out there and it’s getting immensely bigger each year. AI is a tool that’s important, even essential, to be able to look through and analyze all of the digital data that the world is producing each day. Joan Ritter, a business writer at State of writing and Essayroo, tells readers, tech analysts, and traders, that according to her research, “there are estimates that say the digital data will reach 44 zettabytes (trillions of gigabytes) by the end of the year. This is a staggering amount of data that is hard for us to truly wrap our heads around.”

3. AI and the hedge fund industry.

AI is just starting to be applied in the hedge fund industry, but it’s already outperforming the non-AI hedge funds. Some hedge fund managers use AI only for partial input in the trading process so they can still control investment and risk management, but other managers have set up pure AI hedge funds, in which the trading and risk management is all controlled by AI without input from the fund manager. These hedge funds have outperformed all other funds over five, three, and two year periods.

4. Neural trading systems provide better information for trading.

Some tests were done to feed AI five years of data from 1995 to 2000, and AI generated some market predictions for 2001 based on that data. The results of this test determined that neural trading systems that receive historical data can give us much better information for the future, including the next training day. In fact, they outperformed the buy-and-hold portfolio in one year by over 150 percent.

5. AI is able to find manipulations of the market.

Asset management companies can use machine learning systems to pick up acquisitions before they are even announced on the market. AI algorithms can actually spot the acquisitions through tiny signals that indicate there is a bit of insider trading taking place. This has been replicated in other studies, including AI algorithms for cryptocurrencies being able to spot multiple market manipulations based on some behaviors deemed unusual.

6. These AI-powered strategies outperform the traditional approaches.

More research was done in terms of using AI to forecast stock prices. Robert M. Hugh, a tech blogger at Ukwritings and Revieweal, explains to his readers and blog subscribers that “specifically, researchers wanted to compare how AI-powered trading can give results compared to the more traditional buy-and-hold portfolios. This research showed conclusively that that was the case, and in each situation, AI was able to outperform the traditional portfolio approach.”

Blockchain technology is becoming more and more important for experts in the tech industry, regardless of the subject (supply chains, business communication, and more). In reality, blockchain technology is based in trust and collaboration, even more so when artificial intelligence is added to the mix. As opposed to old business models that are more traditional and not AI-based, it’s no longer sufficient to just have an environment of competition in the field of digital and cryptocurrency trading. There now needs to be the right conditions created in which the success of people is dependent on the overall success of the endeavor.

Demand for people with knowledge and skills in artificial intelligence (AI) and machine learning (ML) hugely outstrips the supply.

Fortunately, there are a lot of courses out there to help people up skill themselves. Many are even free.

Here’s a list of ten.

These courses are aimed at a range of different audiences – maybe you want to actually learn how to design and code AI algorithms, maybe you want to bolt together the increasing range of “DIY” AI tools and services that are available, or maybe you need to manage AI projects in your organization. Whatever your needs, you are likely to find something here that will expand your horizons.

For all their power, neural networks have a huge problem that needs to be overcome: how they work is a mystery.

Google has designed a new open-source library intended to crack open the black box of machine learning and give engineers more insight into how their machine learning systems operate. As reported by VentureBeat, the Google research team says that the library could grant “unprecedented” insight into how machine learning models operate.

According to Google research engineer Roman Novak and senior research scientist at Google, Samuel S. Schoenholz, the width of models is tightly correlated with regular, repeatable behavior. In a blog post, the two researchers explained that making neural networks wider makes their behavior more regular and easier to interpret.

Ted Neward explains why (for developers) the next five years will be about language:

Thanks to the plateau of per-chip performance increases and the resulting need to work better with multi-core CPUs, the relative difficulty of mapping user requirements to general-purpose programming languages, the emergence of language-agnostic “virtual machines” that abstract away the machine, the relative ceiling of functionality we’re finding on the current crop of object-oriented languages, and the promise and power of productivity of dynamically-typed or more loosely-typed languages, we’re about to experience a renaissance of innovation in programming languages.

Come hear why this is, and what practicing programmers need to do in order to ride the forefront–instead of the trailing edge–of this new wave in computer science.