Brain games don’t work, but this does…
Remember when “brain training” was all the rage?
The movement started about 12 years ago with the release of Nintendo’s Dr. Kawashima’s Brain Training: How Old is Your Brain?
It went on to sell over 19 million copies and inspired a generation to keep their brains more active.
Back then it seemed everyone had a copy of that game, or at least played Sudoku.
Sudoku was one of the key puzzles in Dr. Kawashima’s Brain Training, and at the time people really believed it was improving their brain function.
After all, Dr. Kawashima was a neuroscientist. And this game was “inspired” by his research.
As time went on and studies were done, it turned out the only thing these “brain training” games enhanced was your ability at playing brain training games.
Play a lot of Sudoku, get better at Sudoku. But not at anything else.
Just over ten years after Dr. Kawashima’s Brain Training hit the shelves, the Global Council on Brain Health and AARP released its report into the long-term effects of these games.
The evidence for the long-term brain health benefits of what most people consider “brain games” is weak to non-existent, according to a new consensus statement issued today by the Global Council on Brain Health (GCBH). The good news is that there are many mentally-engaging activities that can help your brain stay sharp over your lifespan.
“Brain games” can be fun and engaging, but often the claims made by companies touting the benefits of these games are exaggerated. However, there are ways to support and maintain your memory, reasoning skills, and ability to focus, such as engaging in formal or informal educational activities, learning a new language, engaging in work or leisure activities that are mentally challenging, and connecting socially with others.
You can read the full report here.
Activities that actually do enhance your brain
The report also listed a number of things to can do to genuinely enhance your brain:
The GCBH recommends people find new ways to stimulate their brain and challenge the way they think (e.g., learning new skills, practicing tai-chi, taking photography classes, investigating their genealogy). It is also important to participate in mentally-stimulating activities that include social engagement and a purpose in life (e.g., volunteering as a companion and mentoring others in your community). In addition, people should consider physical activities (e.g., dancing or tennis) that involve both mental engagement and physical exercise to improve brain health.
And debunked a number of myths:
Contrary to the many myths about the brain as we age, the GCBH finds:
- You CAN learn new things, no matter your age.
- Dementia is NOT an inevitable consequence of old age.
- Older people CAN learn a second language.
- Older people are NOT doomed to forget things.
One area the report didn’t touch on is the benefits machine learning can provide into this area.
How machine learning can help human learning
Machine learning, it seems, can be used to improve almost any task.
When most people picture machine learning, they think of a chess playing computer. But machine leaning can be used in much more than just chess and strategy games.
Machine learning is the reason why your smartphone can now take better pictures than a consumer-level camera.
It has been used by “algo” traders to vastly improve their investment returns.
And it has recently been used to enhance our knowledge of medicine. Machine learning has proven to be very good at spotting certain diseases and at analysing patient scans.
As Engadget writes:
We already use AI in medicine to examine medical scans and spot signs of diabetes, among other applications. In China, though, artificial intelligence can do more than just assist medical professionals: it can help alleviate the country’s doctor shortage. A hospital in Beijing, for instance, will start running all its lung scans through an algorithm that can expedite the screening process starting next month. The software was developed by a Beijing-based startup called PereDoc, and it can quickly spot nodules and other early signs of lung diseases.
Now, scientists are not only using machine learning to help with diagnosis, but also to find cures.
As my colleague Eoin Treacy writes in his recent Frontier Tech Investor report:
Now the merging of technology and neuroscience is making it possible to develop “digital drugs” that are potentially more precise, more effective and more egalitarian than current medicines and practices.
It’s not something you’d immediately expect, but it’s got the science to back it up and a team of pharmaceutical heavyweights pushing it towards commercialisation.
That second paragraph is key here. Unlike the “brain training” games of the past, these “digital drugs” as Eoin calls them have real scientific evidence backing them up.
And as this digital drug industry matures, Eoin sees it as being a massive threat to traditional pharmaceuticals.
Why? Well the nature of machine learning means that it simply gets better and better.
To draw a comparison to cameras again, a few years ago most photographers thought phone photos were a joke.
Then Google joined the fray.
It dedicated a team to making phone cameras as good, or preferably, better than compact cameras. It did this through machine learning. You can read Google’s in-depth blog about how it works here if you’re interested.
By Google’s second generation of Pixel phones, this tech is so good it can compete with professional photography cameras. It is much, much better than most compact cameras on sale today.
And I’m sure you heard about Google’s AlphaGo beating the world’s best human player in 2016 by a score of 3 to 1.
Google then created a more powerful version of AlphaGo, called AlphaGo Zero, which taught itself Go without being fed information from any human games. It was given the rules and left to teach itself.
The AI engaged in reinforcement learning, playing against itself until it could anticipate its own moves and how those moves would affect the game’s outcome. In the first three days AlphaGo Zero played 4.9 million games against itself in quick succession. It appeared to develop the skills required to beat top humans within just a few days, whereas the earlier AlphaGo took months of training to achieve the same level.
AlphaGo Zero then played the original human-beating AlphaGo program 100 times.
AlphaGo Zero won 100 to 0.
This is the power of machine learning. And this is the power that is now being applied to medicine.
Digital drugs’ main advantage over big pharma
To go back to Eoin’s report. He makes another very good point about the potential of digital drugs and the companies that make them vs transitional pharma companies:
There is a good reason companies like Microsoft, Google and Adobe command high multiples. Software companies are infinitely scalable. All they have to do is create one product and they sell it multiple times.
That is very different from hardware manufacturers. One of the reasons Tesla Motors is having such difficulty is it has to make a new car for each customer.
Hardware is hard. Software is where soft money is made. That also represents a major challenge for the pharmaceutical sector. Where they once dominated the treatment of various ailments, the introduction of software means they will now face an infinitely scalable competitor for the treatment of an increasing number of psychological conditions.
That’s great news for [the company Eoin is tipping] but terrible news for companies like Shire Pharmaceuticals who produces Adderall, the biggest selling amphetamine. Likewise for companies selling anti-depressants. They might finally start to see some competition coming into the market. For companies like Eli Lilly, manufacturer of Prozac, or Pfizer, which manufactures Zoloft, that’s not great news.
You’ll notice I had to cut out the bit where Eoin talks about the company that is developing these digital drugs.
I can’t reprint it here as that wouldn’t be fair to Eoin’s paying subscribers. However, if you want to find out what that company is, and read Eoin’s report for yourself,
It’s a very exciting area with massive possibilities for the future of medicine. I hadn’t realised just how much potential these digital drugs have until I read Eoin’s full report.
I also didn’t realise that the company’s co-founder is also the most cited engineer in human history. With that kind of mind behind it, it’s no wonder the company is coming up with such great innovations.
If this company can continue to apply machine learning principles to medical treatments, the possibilities are endless.
I guess that’s why Eoin is confident its stock could go up in price by as much as 500%. As he says, “there is huge potential here”.
I highly recommend reading Eoin’s digital drug report, as much for the insight he provides as the investment potential. If you want to read it for yourself, here’s everything you need to know.
Until next time,
Editor, Exponential Investor