Data entry and processing is one of the key areas where Artificial Intelligence (AI) is expected to have a major impact in the coming years. With the increasing amount of data being generated every day, the demand for faster and more efficient data processing has never been higher. Fortunately, AI technology is here to help meet this demand and take data entry and processing to the next level.
One of the main advantages of AI in data processing is its ability to automate manual data entry. This means that instead of relying on human data entry clerks, AI algorithms can process and categorize vast amounts of data much more efficiently and accurately. AI algorithms can also identify patterns and relationships within the data, allowing for more comprehensive data analysis.
Another key area where AI is expected to enhance data entry and processing is in natural language processing (NLP). NLP is a subfield of AI that focuses on the interactions between computers and humans in natural language. With advancements in NLP, AI will soon be able to understand and interpret written and spoken human language, making data entry and processing even more seamless.
Before 2025, we can expect to see significant advancements in AI’s ability to process and analyze unstructured data, such as images, videos, and audio. AI algorithms will be able to automatically identify and categorize information within these types of data, making data entry and processing much easier and more efficient. Additionally, AI will be able to process multiple languages, further expanding its reach and impact on data entry and processing.
Another exciting development in the field of AI and data entry and processing is the use of machine learning. Machine learning is a type of AI that allows algorithms to learn and improve over time through experience. With machine learning, AI algorithms can become more accurate and efficient at processing and analyzing data, reducing the risk of human error and improving the overall accuracy of the data.
In conclusion, the next few years will bring significant advancements in the field of AI and data entry and processing. From automating manual data entry to processing unstructured data and utilizing machine learning, AI has the potential to greatly enhance the accuracy and efficiency of data processing. By embracing these changes, we can look forward to a future where data entry and processing is seamless and accurate, providing valuable insights and helping organizations make better data-driven decisions.
These days it seems like businesses are trying to use AI to do everything. At least for startups, that isn’t far off. Anywhere there is a dataset remotely large enough and an answer that is vaguely definable, companies are putting together a business model to use machine learning to solve the problem. With some incredible successes in areas like image classification and defeating humans at video games, its hard not to be impressed.
One of the best channels for following recent breakthroughs in AI is the 2 Minute Papers YouTube Channel, started by Károly Zsolnai-Fehér, a professor at the Vienna University of Technology in Austria. Károly’s videos combine interesting clips of the programs in action with well-delivered summaries of recent papers illustrating advances in artificial intelligence.
In one of his latest videos, he covers an AI that not only can copy the most successful actions that humans take in video games but can actually improve on those actions to be better than the best human players. So does that mean that AI will be displacing office workers once it learns how to do their jobs better than them? Probably, yes. But maybe not quite how you think it might.
As much of a ‘black-box‘ as AI has been in the past, modern systems are becoming better and better at explaining how they arrived at an answer. This gives human operators predictive capabilities that we didn’t have with systems of the past that could spit out an answer but gave us no indication of how that answer was formulated.
This Forbes article on Human-Centric AI provides some examples of how modern AI systems can be implemented to train employees to do their jobs better and even enjoy their jobs more while doing it! If that doesn’t sound incredible to you, you may be a machine who is only reading this page to improve your search algorithm.
So what does this all mean? A lot of research is showing that AI is actually creating many more jobs than it destroys. So, as long as you’re willing to try and understand the systems that will one day be our overlords, you should be able to upgrade your career and stay employed.
Whether you still want the job that remains is another question entirely.
Index funds are the most highly traded equity investment vehicles, with some funds like ones created by Vanguard Group cumulatively being valued at over $4 Trillion USD. Index funds have democratized investing by allowing access to passive investments for millions of people. But what are they?
An index fund is a market-capitalization weighted basket of securities. Index funds allow retail investors to invest in a portfolio made up of companies representative of the entire market without having to create that portfolio themselves. Compared to actively managed funds like mutual funds and hedge funds, index funds tend to have much lower fees because the only balancing that happens occurs based on an algorithm to keep the securities in the fund proportional to their market cap (market capitalization, or market cap, is the number of shares that a company has on the market multiplied by the share price).
Starting in the 1970s, the first ‘index funds’ were created by companies that tried to create equally weighted portfolios of stocks. This early form of the index fund was abandoned after a few months. It quickly became apparent that it would be an operational nightmare to be constantly rebalancing these portfolios to keep them equally weighted. Soon companies settled on the market capitalization weighting because a portfolio weighted by market cap will remain that way without constant rebalancing.
With the incredible advancement of AI and extraordinarily powerful computers, shouldn’t it be possible to create new types of ‘passively managed’ funds that rely on an algorithm to trade? What that could mean is that index funds might not have to be market cap weighted any longer. This push is actually happening right now and the first non-market cap weighted index funds to appear in over 40 years could be available to retail investors soon.
But this means that we need to redefine the index fund. The new definition has three criteria that must be met for a fund to meet:
It must be transparent – Anyone should be able to know exactly how it is constructed and be able to replicate it themselves by buying on the open market.
It must be investable – If you put a certain amount of money in the fund, you will get EXACTLY the return that the investment shows in the newspapers (or more likely your iPhone’s Stocks app).
It must besystematic – The vehicle must be entirely algorithmic, meaning it doesn’t require any human intervention to rebalance or create.
So, what can we do with this new type of index fund?
“Sound Mixer” board for investments with a high-risk, actively traded fund (hedge fund) on the top and lower risk, passively traded fund (index fund) on the bottom.
We can think of investing like a spectrum, with actively managed funds like hedge funds on one side and passively managed index funds on the other and all the different parameters like alpha, risk control and liquidity as sliders on a ‘mixing board’ like the one in the image above. Currently, if we wanted to control this board, we would have to invest in expensive actively managed funds and we wouldn’t be able to get much granular control over each factor. With an AI-powered index fund, the possibilities of how the board could be arranged are endless. Retail investors could engage in all sorts of investment opportunities in the middle, instead of being forced into one category or another.
An AI-powered index fund could allow an investor to dial in the exact parameters that they desire for their investment. Risk, alpha, turnover, Sharpe ratio, or a myriad of other factors could easily be tuned for by applying these powerful algorithms.
The implications of a full-spectrum investment fund are incredible. Personalized medicine is a concept that is taking the industry by surprise and could change the way that doctors interact with patients. Companies like Apple are taking advantage of this trend by incorporating new medical devices into consumer products, like with the EKG embedded into the new Apple Watch Series 4.
Personalized investing could be just as powerful. Automated portfolios could take into account factors like age, income level, expenses, and even lifestyle to create a portfolio that is specifically tailored to the individual investor’s circumstances.
So why can’t you go out and purchase one of these new AI managed, customizable index funds?
Well, unfortunately, the algorithms do not exist, yet. The hardware and software exists today to do this but we’re still missing the ability to accurately model actual human behaviour. Economists still rely on some pretty terrible assumptions about people that they then use to build the foundations of entire economic theories. One of these weak assumptions is that humans act rationally. Now, there is a lot of evidence to suggest that many people act in the way that we are programmed to by evolution. The problem is, a lot of what allowed us to evolve over the last 4 billion years of life on earth, is pretty useless for success in 2018-era financial planning and investment.
All hope is not lost, however. New research into the concept of bounded rationality, the idea that rational decision making is limited by the extent of human knowledge and capabilities, could help move this idea forward. One of the founding fathers of artificial intelligence, Herbert Simon, postulated that AI could be used to help us understand human cognition and better predict the kinds of human behaviours that helped keep us alive 8,000 years ago, but are detrimental for wealth accumulation today.
By creating heuristic algorithms that can capture these behaviours and learning from big data to understand what actions are occurring, we may soon be able to create software that is able to accentuate the best human behaviours and help us deal with the worst ones. Perhaps the algorithm that describes humanity has already been discovered.
Bobby Axelrod, the main character on the popular Finance drama, Billions, is a lot like Tesla CEO Elon Musk. They’re both billionaires. They both draw substantial public praise and criticism and are highly divisive figures who have a large impact on their respective industries. They were also both investigated and charged by the SEC (and in Axelrod’s case, the US Justice Department) for actions related to securities law. The main difference between the two? Bobby Axelrod is a fictional character whose proclivity for conflict is only superceded by his complete lack of restraint when his life and freedom are on the line. In real life, the consequences of your actions are permanent and making deals in the business world often means compromising, negotiating, and settling.
Today (September 29, 2018) Elon Musk settled with the SEC. He will no longer be chairman of Tesla, for at least three years, and will pay a fine in excess of $20 Million. In all, it is a relatively lesser penalty than the lifetime ban from being CEO of a publicly traded company that the SEC was seeking. It is also a larger punishment than someone who has not committed any wrongdoing deserves. Depending on your perspective, Musk either got away easy or was unfairly chastised by the state for a 60 character tweet.
Of course, the civil settlement does not preclude the Justice Department from filing criminal charges against Elon at a future date. However, a criminal trial has a much higher burden of proof than a civil case, which can be decided based on a balance of probabilities. In a criminal case, the prosecution must prove, beyond a reasonable doubt, that the defendant committed the alleged crimes, whereas, in a civil suit, all that is required is a greater than 50% probability that the act took place.
In a previous post from September 27, we discussed whether AI could play a role in predicting the outcome of cases like this, perhaps assisting traders in making appropriate investment decisions surrounding companies with legal troubles. Despite a strong performance in short-term volume trading, automation has not yet played a large role in the fundamental analysis of a stock’s long-term viability. Most AIs that trade today are relying on purely technical analysis, not looking at any of the traits that make a company likely to succeed, but instead relying on historical price data to predict trading and movement patterns.
Fundamental analysis is complex and subjective. Even the smartest deep neural networks would have a difficult time distinguishing between the very human aspects that go into valuing a company. The problem with AI, in this particular application, is that it would require a broad knowledge of various domains to be combined in order to predict with any degree of accuracy. Right now, even the best deep neural networks are still very narrowly defined. They are trained to perform exceptionally well within certain contexts, however, beyond the confines of what they ‘understand’ they are unable to function at even a basic level.
Complexity in neural networks results in ‘overfitting’ – networks specify the training set well but fail at more generalized tasks.
In the above example, we can see how more complicated neural networks might fail to understand topics that are even slightly different from what they have seen in the past. The model fits the data that the network has already encountered, however, this data does not reflect what could happen in the future. When something happens that they haven’t encountered before (a CEO tweets something about 420, for example), a human can immediately put that into context with our everyday experience and understand that he’s likely talking about smoking weed. However, an AI trained to predict share prices based on discounted cash flow analysis would have absolutely no clue what to do with that information.
It is likely that there are companies working on technology to help train neural networks to deal with the idiosyncratic information present in everyday business interactions. One possible answer is to have multiple neural networks working on different subsets of the problem. Similar to how deep neural networks have enabled advances in fields ranging from medical diagnosis to natural language processing, new organizations of these systems could enable the next generation of AI that is able to handle multiple tasks with a high level of competency. As we continue to build this technology, we’ll keep speculating on whether or not an executive is guilty, and traders and short-sellers will continue to make and lose billions based on the result.
The big news from the tech and finance world on September 27, 2018, is that Elon Musk has been sued by the US Securities and Exchange Commission (SEC) for his tweets about taking Tesla private at $420 per share.
Am considering taking Tesla private at $420. Funding secured.
The SEC is seeking to have Musk banned from serving as an officer or director of any public company. Their reasoning? Musk was lying about having funding secured. This implies that he was trying to manipulate Tesla’s share price in an upward direction. Well, it worked, for about a day, that is. On the day of the tweet, Tesla’s share price rose to a high of $379.87 US per share from its previous price of around $350 per share, before falling back to $352 the next day (August 8, 2018). If the markets had actually believed Musk’s Tweet, Tesla’s share price likely would have climbed closer and closer to the mythical $420 price as the take-private day neared.
Tesla’s share price peaking after Musk’s announcement.
Instead, Tesla’s share price dropped like a rock because every savvy investor realized that Musk’s statement was either pure fanciful bluster, a joke about weed, or both. Of course, today has been much worse for Tesla’s share price than any of Musk’s recent ill-advised tweets. In after-hours trading, Tesla’s share price is down as much as 13%. That’s a lot and it is falling dangerously close to their 52 week low. This is all especially troubling considering that Tesla is expected to announce their best quarter ever, in terms of cash flow, in a few days.
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So, what is the SEC doing, was it possible to predict this, and could AI make this type of situation any better? The answer to the first question is unclear, however, the answer to the second two questions is likely, yes.
AI is already being used in the legal profession to help identify responsive documents that must be turned over to the opposing party during a lawsuit. MIT Professor Emeritus Frank Levy leads a research that helps law firms apply machine learning software to the practice of law.
If AI can predict what documents will be useful in a lawsuit, then whenever the CEO of a publicly traded company does something suspicious, it should be possible to use these same programs to parse historical cases and see what precedent there is for a lawsuit to be filed. At the very least, it could provide some insight into the likelihood of an indictment and, in the future, could even suggest potential courses of action for a company to take if it found itself in this type of situation.
Would the AI be able to help predict whether or not Elon will be convicted? Possibly. While I am not aware of any AIs currently being used to predict the outcome of legal matters, in my September 24, 2018 column, I covered the AI that perfectly predicted the outcome of last year’s Superbowl. While legal cases may be more complicated than a football score, there is likely several orders of magnitude more data about the outcome of various lawsuits than there is about football players, simply because there are WAY more lawsuits than there are football teams.
From a financial perspective, we could use this type of AI to predict potential lawsuits and their results and train the AI to make trades based on these predictions. If these types of AI were already in use, we could expect much smoother and more predictable share prices as the effect/implications of a particular news story would become apparent almost immediately after the information surfaces.
For now, I’ve programmed a simple AI for Elon Musk to help him decide if he should tweet something or not. You can try it, too, if you’d like. It’s posted below:
Tiger Woods won his 80th PGA tour title this Sunday, September 23. I was planning to delve deeper into my MIT course on AI and study the details of natural language processing, specifically syntactic parsing and the value of training data. Instead, I found myself glued to a browser window for four and a half hours this afternoon, watching my favourite golfer relive his glory days, winning by 2 shots and capturing the Tour Championship. It was totally worth every minute.
You see, even though humans are being vastly outpaced by AI and machines at every turn, humans are still better at many nuanced tasks. Sure, you can program a robot to swing a golf club and hit repeatable shots, but even the best golf robots still can’t beat the best humans over 18 holes with all the nuanced shots required for a round. Still, they can make a hole-in-one from time to time:
Despite humanity’s increasing incompetence compared to machines, it is still incredibly fun to watch a talented person, who has worked their entire life to perfect their craft, get out there and show the world what they’ve got. Doubly so if that person has recently recovered from spinal fusion surgery and hasn’t won for over five years on tour. Yeah, it’s just putting a little white ball in a hole, but the crowds and excitement that Tiger Woods is able to generate while he plays are unparalleled in golf, and possibly even in sports.
Tiger didn’t win the FedEx Cup, the PGA Tour’s season-long points-based title, but he came really close. If he had, he would’ve made $10,000,000 on the spot. Not too shabby. Regardless, with the highest viewership numbers in the history of the tournament and crowds so large that commentators said they’d never seen anything like it, Tiger Woods undoubtedly made the tour, its sponsors and network partners well over $10 Million this weekend. The amount of value that he generates for the tour and for golf is almost incalculable.
Of course, if you’re not a golf fan, you probably think that it’s boring to watch. That can be said about just about any sport or event that one doesn’t understand. Something is boring to us because we don’t understand the context, the history, and the implications of a certain event happening. Once we understand the subject and can opine and converse with other people about the topic then it becomes much more real and tangible.
I think the same principle applies to artificial intelligence as well as finance. Few understand the topic. It takes time to learn and understand the nuances that make the topics interesting and valuable. Once one does build the knowledge and expertise to apply skills in these areas, the results can be extraordinary.
So I’m going to pose a question for my future self and any would-be AI experts. In 2 years, will we be able to build software that can perfectly predict the outcome of major events in sports, specifically golf tournaments, with better results than the best human statisticians and algorithms?
According to some experts, in 2018, natural language and audio processing (NLP for short) are the two areas that AI excels most at. It seems fairly obvious that machine audio processing would be good when you look at the number of US homes with a smart speaker device like Amazon’s Echo, Apple Home Pod, or Google Home (nearly 50%). That’s a lot of people with a device whose sole purpose is to collect data on what you’re saying and translate it into usable commands for you, and into monetizable data for the company that builds them.
“Hey, Siri!”
— me, just now
In fact, if you read that out loud right now, there is a good chance that your phone just pinged at you wondering what you want from it. With so many devices listening constantly to our every conversation and vocal machination, is it any wonder that they’re starting to understand us better than we understand ourselves?
In my home, I have Apple Home, and by proxy, Siri, set up to control everything from my locks to my lights, smoke alarm, security system and thermostat. If Siri ever became sentient and went Skynet on my ass, she could do A LOT of damage. Although, based on the latest tests, I’m much less worried about Siri taking over than I am about Google. Or more specifically Google’s Duplex AI. It can literally trick humans working at shops into believing that it is a person. If that’s not scarily impressive, I don’t know what is.
Are we doomed? Probably not… Yet. Duplex is still very limited according to Google. All it can do is book appointments for you. But people got so scared that they’d be talking to a machine and not know it that the outcry forced Google to hamper its own system. Google has announced that Duplex will now announce itself as a computer at the beginning of every phone conversation so as not to creep people out. To me, this kind of defeats the purpose of the software to begin with. But hey, it’s probably bad form to test your AI on a bunch of unsuspecting hair salon and restaurant employees without them knowing about it.
This software is getting really good at understanding and communicating with humans via voice. This is mainly because it has a TON of really good data collected by millions of devices around the world. It’s also because engineers, mathematicians, and programmers have made some serious breakthroughs in Deep Learning in the past 10 years and Apple and Google have tens of billions of dollars invested into making it work.
This weekend I’ll be learning from MIT Professor Regina Barzilay about what it means for machines to understand something. We’ll also be covering which NLP problems have been solved, where progress is being made and tasks that are still very difficult for computers to solve. Hopefully, I’ll come back with a better understanding of how it all works (I think it has something to do with phonemes and triphones) and what we can do with it. Once I do, I’ll report back here and let you know how close we are to the robopocalypse.
