Alla Petriaieva's Avatar
Alla Petriaieva
published in Blogs
Mar 06, 2021
Can A.I. Help Treat Mental Health?

Can A.I. Help Treat Mental Health?

Artificial intelligence is no longer just a staple of the page and movie screen – it is increasingly and creatively used in a variety of real-world contexts. Scientists are using AI to tackle a pressing, worldwide challenges, including an often-overlooked but very serious problem – treating mental health.

Backed by sobering statistics, the world is more aware of the importance of maintaining mental health than ever: the National Institute of Mental Health says that one in five American adults battles mental illness; meanwhile 15.5 percent of the world is afflicted in some way. According to the Henry Kaiser Family Foundation, roughly 40 percent of Americans lack suitable access to mental health professionals. Treatment is expensive when available; specialized treatment often moreso.

AI offers a solution, enhancing the capabilities of human mental health professionals while filling in gaps in treatment. Forbes contributor Bernard Marr recently offered multiple examples of AI’s potential to assist doctors and people alike.

One exciting possibility is to leverage the power of algorithms to analyze patient data. Putting algorithms to work for analysis means the ability to look at amounts of information too massive and impossibly time-consuming for humans to parse on their own, then identifying problems and propose treatment options to fit based on those insights.

The analytic ability could be augmented using chatbots. Humans are already used to interacting with chatbots in a customer service capacity, lending them an air of familiarity. Chatbots would offer the benefit of anonymity, encouraging people to talk more openly and honestly about what they are experiencing, while also offering unparalleled accessibility – not only would a mental health chatbot be accessible at any time, it would give inexpensive (or free) access to mental health services for the people who lack it without physically visiting a doctor’s office.

The possibilities are exciting, but obstacles remain. Algorithmic bias – that is, bias implicit in algorithms that reflects the conscious or subconscious biases of their programs – is a real and potentially damaging phenomenon. Pearl Chiu, a psychologist at the Fralin Biomedical Research Institute at Virginia Tech Carilion, told The Verge that she works to minimize bias by “keeping everyone involved…‘blind to as many things as possible’” – an approach that would need to be standardized and applied in all instances. There is also the issue of developing a truly defined set of parameters for diagnosing mental illness, which is more difficult than diagnosing something based on physical evidence.

These challenges, however, are not a deterrent. Data sharing is being used to increase sample size and limit the possibility of false positives; protocols can be developed to ensure AI tools behave in an unbiased way. A future where AI treats mental health is not farfetched – it is well on its way to becoming reality.

The Investment and Financial Industry Faces the Same A.I.-Driven Revolution

Hedge funds and large institutional investors have been using Artificial Intelligence to analyze large data sets for investment opportunities, and they have also unleashed A.I. on charts to discover patterns and trends. Not only can the A.I. scan thousands of individual securities and cryptocurrencies for patterns and trends, and it generates trade ideas based on what it finds. Hedge funds have had a leg-up on the retail investor for some time now.

Not anymore. Tickeron has launched a new investment platform, and it is designed to give retail investors access to sophisticated AI for a multitude of functions:

And much more. No longer is AI just confined to the biggest hedge funds in the world. It can now be accessed by everyday investors. Learn how on Tickeron.com.

Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Mar 07, 2021
4 Tricks Hedge Funds Use to Get Ahead

4 Tricks Hedge Funds Use to Get Ahead

If the stock market were Major League Baseball, hedge funds and institutional investors would be the pros on championship teams while everyday self-directed investors (SDIs) are the benchwarmers in the minors.It’s how they get ahead, and it’s why 90% of SDIs lose money trying to play (invest and trade) in the major leagues. The 4 tricks we discuss below are rooted in one common theme: they all use Artificial Intelligence and algorithms to generate data and ideas.
John Jacques's Avatar
John Jacques
published in Blogs
Mar 22, 2018
A.I. Stock Market Predictions: Head & Shoulders

A.I. Stock Market Predictions: Head & Shoulders

Statistics for the Head-and-Shoulders Bottom Pattern The days where only hedge funds used algorithms to trade stocks are officially over. Now retail investors can use Artificial Intelligence (A.I.  Here’s an example of the algorithm in action: Late last year, Tickeron’s A.I.
Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Jul 10, 2020
3 Stocks to Buy if Coronavirus Second Wave Hits

3 Stocks to Buy if Coronavirus Second Wave Hits

By analyzing market trends from the first wave, you can predict behavior for the second. Technology stocks have performed at historic levels this year, but the market is severely overbought.To compensate for that, look at performance during Q1 and Q2, the height of global Covid shutdowns.
Edward Flores's Avatar
Edward Flores
published in Blogs
Feb 06, 2021
How to Become the Millionaire Next Door

How to Become the Millionaire Next Door

The Golden Gate Bridge is always a fixture of these walks too, one of man's most beautiful creations.  As we were walking, at one point she turned to me and said, "Man, I'll never have a million dollars."" My girlfriend is 27 years old and works as a graphic designer, making about $75,000 a year.
Alla Petriaieva's Avatar
Alla Petriaieva
published in Blogs
Feb 23, 2021
Is Ethereum’s Bomb about to Explode?

Is Ethereum’s Bomb about to Explode?

Ethereum’s software is set for an update in October.Until it is finished, participants in the Ethereum blockchain must determine how to delay the difficulty bomb – code that necessitates a steadily increasing amount of computer power to mine blocks and unlock rewards – that is already in place.
Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Aug 07, 2018
When Is the Next Recession Coming?

When Is the Next Recession Coming?

However, we also know that economists predicted 22 recessions out of 11 that took place since 1945. Are there real recession signs we should watch for?Indeed, the answer is yes, and here are a few very important ones: The first one is almost obvious and known to everyone – it is the Fed.
Abhoy Sarkar's Avatar
Abhoy Sarkar
published in Blogs
May 22, 2020
Central banks have been buying $2.4 billion in assets every hour for the past two months

Central banks have been buying $2.4 billion in assets every hour for the past two months

Some $17.8 billion has been poured into  bond markets over the past week, the biggest move in more than three months.Around $3.5 billion has been invested into gold, the second largest on record. 
Rick Pendergraft's Avatar
Rick Pendergraft
published in Blogs
Feb 07, 2021
Mid-January Short Interest Report Shows 8 Stocks with Good Fundamentals and High Short Interest
Sergey Savastiouk's Avatar
Sergey Savastiouk
published in Blogs
Mar 10, 2021
How to Start Trading Penny Stocks

How to Start Trading Penny Stocks

Penny stocks have long been marginalized within the professional investment community, oftentimes being painted with a broad brush of simply being “too risky.” Leonardo DiCaprio’s depiction of the penny stock peddling conman, Jordan Belfort, in the Wolf of Wall Street certainly didn’t help.Here are four reasons to start trading them now. Reason #1: Let’s State the Obvious -- Penny Stocks are Cheap A single share of Apple Inc. costs over $350.
Abhoy Sarkar's Avatar
Abhoy Sarkar
published in Blogs
May 08, 2020
US unemployment rate jumps to 14.7%, the highest in series history

US unemployment rate jumps to 14.7%, the highest in series history

The U.S. economy’s employment fell by -20.5 million in April. The coronavirus crisis led to unemployment rate soaring to 14.7% in the U.S, the highest rate in the Bureau of Labor Statistics-tracked series history that goes back to 1948. However, the figures were better compared to several economists'/analysts' forecasts of 22 million job losses and 16% unemployment rate.  Another unemployment measure that includes those who have stopped looking for work as well as those holding part-time jobs for economic reasons also touched an all-time high of 22.8%.