January 5, 2021
In Today's Issue:
  • Ethereum, the other big cryptocurrency's price spike
  • Video games and the science of goal setting
  • Next big developments in AI and deep learning
  • What Else is Going On
  • News You Can Use
Remember Ethereum, the Other Big Cryptocurrency?
We’ve all been reading and seeing a lot of news about Bitcoin’s historic price increase, but not so much about Ethereum’s, which has been even more dramatic.

Ethereum is up even more than Bitcoin
Ethereum is up from $134 in January 2020 to $1,016 on January 4, 2021, which is a price increase of around 758%. 
Bitcoin is up from $7,345 on January 4, 2020 to $31,532 yesterday, a price increase of around 429%.
Of course, ETH probably would not have increased without the rise of Bitcoin, but nonetheless, ETH is hardly getting the headlines that BTC is. So hey, we thought we should write something about it.
Vitalik Buterin’s take
The cofounder of Ethereum and a respected cryptocurrency authority, Vitalik recently wrote a blog post about the ‘underrated’ Bitcoin and cryptocurrency case.
One of Vitalik’s proclamations from the blog post (our personal favorite):
"One of the more underrated bull cases for cryptocurrency that I have always believed is simply the fact that gold is lame, the younger generations realize that it's lame, and that $9 trillion has to go somewhere."
The $9 trillion is the total value of the world's mined gold.  These are the kinds of observations you will get in the rest of the post as well, so it’s probably worth a read, if for nothing more than the originality of its ideas.  We’ve laid out a few more gems from the post below.
The beginning of the blog
Here is another excerpt from the beginning of the post:
“After months of fighting what may perhaps even be humanity's first boss-level enemy since 1945, the city itself is close to normal, though the world as a whole and its 7.8 billion inhabitants, normally so close by, continue to be so far away. Many other parts of the world have done less well and suffered more, though there is now a light at the end of the tunnel, as hopefully rapid deployment of vaccines will help humanity as a whole overcome this great challenge.”
Boss-level enemy…classic that he is comparing the global chaos to a tough bad guy from a video game.
And the conclusion of the blog is similarly legend
“So we have a world where:
  • One-to-one interactions are less important, one-to-many and many-to-many interactions are more important.
  • The environment is much more chaotic, and difficult to model with clean and simple equations. Many-to-many interactions particularly follow strange rules that we still do not understand well.
  • The environment is dense, and different categories of powerful actors are forced to live quite closely side by side with each other.
And finally it's a very multidisciplinary world, one that is much harder to break up into layers and analyze each layer separately. You may need to switch from one style of analysis to another style of analysis in mid-sentence. Things happen for strange and inscrutable reasons, and there are always surprises. The question that remains is: how do we adapt to it?”
How is it that the media is not covering this guy, and more importantly, the massive price rally of ETH more?
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Are Video Games Really the Model for Successful Goal-Setting?

Ok, it’s the first full week of the New Year, so everybody is gearing up to make 2021 a banner year, both personally and professionally.  Good intentions…but picture this all-too-common scenario:
Jan 1, morning – You set a goal to read more books and get in the best shape of your life this year.
Jan 1, evening – You are glued to the couch eating takeout pizza and binge-watching episodes of The Queen’s Gambit.
Where did it all go off the tracks?
According to an article in The Conversation, the so-called common wisdom is to follow the SMART model which tells you to set specific goals, make the goals somewhat difficult and get regular feedback on progress.  Check, check and check.  If you’re wondering, SMART stands for specific, measurable, achievable, realistic and timebound.  A common example is walking 10,000 steps a day.
But this advice is so 1990s.  Video games popularized this model which eventually led to the concept of gamification.
New, more modern research suggests that this system of SMART goal setting may not be the optimal way to do things.
Here’s what to do to kill it in 2021 with your goals, according to the latest scientific research.
Set “open” goals
Open goals are not specific and are exploratory.  The idea behind setting such goals is often “let’s see how well I can do.” 
Take some shots off your golf game.  For example, professional golfers in one study described performing at their best when aiming to “see how many under par I can get.”
Climbing Mount Everest.  One elite athlete interviewed in another study who was a Mount Everest climber described his approach as follows:
“I was just thinking, ‘Oh I’ll just see how it goes and take it as it comes.’ I climbed higher and higher and the climb had got more and more engrossing and difficult and all-encompassing really […] until I discovered that I’d climbed like 40 metres without consciously knowing what I was doing.”
Get rid of the feeling of failure.  A participant in follow-up studies said open goals “took away the trauma of failing.”  If you think about it, it makes total sense.  You set a goal of walking 10,000 steps or losing 5 pounds, or whatever, and miss the goal by a hair and feel like you’ve failed.  That’s not a positive mindset that can lead to a virtuous cycle of behavior.  It’s just depressing.
Doing something now versus planning to get something done in the future
Another important difference between open and SMART goals is the perspective – SMART goals are about identifying something in the future you want to achieve, not about what you are doing now.  And the more difficult the goal, the bigger the gap between where you are at right now and where you want to be.  So that means progress towards the goal will likely be slow, which is not that much fun.
On the other hand, the focus of open goals is on your starting point. “Let’s see how much weight I can lose.  Oh, I lost 1 pound this week, cool, let’s keep doing this.”  It’s subtle, but the different mindset makes you enjoy incremental progress and continue that positive behavior instead of failing to meet a lofty goal.

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Artificial Intelligence Bigwigs Weigh In on the Next Big Developments in AI in 2021

Hint:  It’s more than just OpenAI and GPT-3.

What do you get when the big luminaries converge at Montreal.AI for a pow-wow on the future of AI?  Answer:  a lot of information that someone needs to translate for people without code in their veins or a PhD in cognitive psychology.
If you want the unabridged version, you can check out the 3 hour 49 minute Zoom call replay that is available on Montreal.AI’s Facebook page.  There are 16 speakers from the top of the artificial intelligence/deep learning food chain on the call replay.
The big question they are addressing:  are big data and deep learning enough to get to artificial general intelligence?
Here is a summary of the answers given as well as who gave them, based on reporting in Venture Beat:
1.  Hybrid AI
Gary Marcus, a preeminent computer and cognitive scientist
His idea:  He proposed a hybrid approach that combines learning algorithms with rules-based software.  Rule-based software is basically if-then logic.  If you do this, you get that.  It’s the opposite of what deep learning does which is attempt to go from specific to general based on the training models of data that you feed the AI/deep learning model.
2.  Evolutionary theory as inspiration
Image classification and computer vision have driven the advances in deep learning of the past decade.  But more is needed; human intelligence comes from perception and interaction with the real world. 
OpenAI researcher Ken Stanley
His ideas:

  • “There is a fundamentally critical loop between perception and actuation that drives learning, understanding, planning, and reasoning. And this loop can be better realized when our AI agent can be embodied, can dial between explorative and exploitative actions, is multi-modal, multi-task, generalizable, and oftentimes social,”
  •  “There are properties of evolution in nature that are just so profoundly powerful and are not explained algorithmically yet because we cannot create phenomena like what has been created in nature,” Stanley said. 
3.  Reinforcement learning
Reinforcement learning is the training of machine learning models to make a sequence of decisions for a given scenario.
Richard Sutton, DeepMind and pioneer of reinforcement learning
His ideas:
  • Sutton and DeepMind, the AI lab where he works, is deeply invested in “deep reinforcement learning,” a variation of the technique that integrates neural networks into basic reinforcement learning techniques. This is the variation of deep learning that DeepMind used to master games such as Go, chess, and StarCraft 2.
  • “Reinforcement learning is the first computational theory of intelligence.  Reinforcement learning is explicit about the goal, about the whats and the whys. In reinforcement learning, the goal is to maximize an arbitrary reward signal. To this end, the agent has to compute a policy, a value function, and a generative model,” Sutton said. 
In deep reinforcement learning, agents are given the basic rules of an environment and left to discover ways to maximize their reward.
4.  Common sense and integrating general knowledge
Mark Twain’s refrain about common sense is doubly true about AI and deep learning models. 
An example
Here is a common example (see what we did there?) of how dumb even sophisticated AI sometimes seems:
A state-of-the-art model when asked to create a sentence by using the words "dog, frisbee, throw, catch" came up with "Two dogs are throwing frisbees at each other."

The model was fed the following information: "a person throws a frisbee and a dog catches it," but can't get it right without common sense understanding about the real world.  In normal conversation, you don’t have to explain everything, ie. that dogs can’t throw frisbees, but AI is not there yet. 

University of Washington professor Yejin Choi 
Her idea: 
“We know how to solve a dataset without solving the underlying task with deep learning today,” Choi said. “That’s due to the significant difference between AI and human intelligence, especially knowledge of the world. And common sense is one of the fundamental missing pieces.”

GPT-3 alone cannot be the future
NLP and AI model landscape will evolve.  For now, OpenAI is top of mind as many new startups are being built on the strength of the GPT-3 model.

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Slack is slacking.  Slack experienced widespread service disruptions on its first full workday of the year.

Tesla roars into New Year.  The stock soared past $700 billion in market value after nearly hitting 500,000 deliveries in 2020.

Picking the carcass.  Quibi is in advanced talks to sell its content catalog to Roku Inc. 
Ok, well everything is fine, then.  Microsoft says hackers viewed source code but didn't change it.
Chinese telecom stocks getting the boot.  China Mobile, China Telecom and China Unicom will all be suspended from the NYSE by January 11.
Get in a better mood despite Covid.  Boost your mood drug-free by learning more about stress and how to conquer it.
Trends in science in China.  This article asked several Chinese researchers about what they are working on and where their field is heading in 2021.

Learning from failure.  The biggest technology failures of 2020, explained.
Return of the SPAC

Bitcoin Roaring in the 20s

Three Sectors to Watch in 2021 Being Driven by Tech Originating Outside the U.S.
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