AI is fascinating, but most organizations don’t have the magnitude of data or the skills to effectively leverage it.
Watching Qlik’s Qonnections 2018 keynote yesterday opened my eyes to a new application of ai. Qlik claimed that Qonnections was the largest data analytics conference in the world. Not sure about that one, but their existing 45,000 customers is hard to argue. Their CEO make some interesting claims, that 10 of the 10 largest pharma companies use Qlik, along with an impressive list of the top 10 companies across several leading industries.
The progression is always like this: data -> analytics -> insights. I’ve heard it many ways from many suppliers, but the two biggest hurdles with data are always the same. Gathering high-quality data is difficult. There are lots of tools for display once clean data is available. Regardless of the quality of the tools, deriving actionable insights is tricky.
It all starts with data literacy and a data-centric culture.
Qlik’s director of research presented their soon to be released “Cognitive Engine”. The highlight of the cognitive engine is their clever use of AI. With Qlik’s new technology, a researcher can visually drill into apparent anomalies in the data, and Cognitive Engine’s AI will proactively identify statistically significant variances worth exploring.
This, to me, was fascinating. It was clearly one of the quickest and easiest ways a data-centric organization could start to leverage AI.
One of the highlights of attending the GPU Technology Conference in San Jose back in March was the keynote address by NVIDIA CEO Jensen Huang. Of course, there was the predictable push to sell more hardware, but notwithstanding the technology demonstrations were truly impressive.
There was an autonomous car in the parking lot. There was a driver in the conference room. Then, on the big screen, was the driver in the holodeck – the simulated environment for the real car out back. The real driver, in the virtual holodeck, seeing real-time data and imagery, drove the virtual car, which in turn drove the physical car safely into a parking spot. It was impressive. The processing power required to do this all real-time is barely imaginable.
But the biggest insight for me came from NVIDIA’s Director of Developer Programs, William Ramey.
AI is not Magic.
With all the mystery around Machine Learning and Deep Learning in particular, it was insightful to hear such a thing. The problem, of course, is that the hype is so great. It’s easy to assume that because this problem is hard, AI must be the answer.
His statement boiled down to this: If X then Y. Can the problem be defined so simply? Can a human understand, at least conceptually, the process? Is there a defined outcome? If all are true, then AI is likely a viable candidate. If a human can’t articulate the process, then don’t expect AI to provide an answer.
Ultimately it boils down to this: Could a human, with a lot of data and a lot of time, solve this same problem? If not, don’t expect magic from AI.
Amazon’s webinar today highlighted several ways that Big Data is being used in the HCLS industry. One of the key insights was the self-realization within one of the featured medical practices that they were “data rich, insight poor.” Having tremendous amounts of data isn’t much value to anyone without the appropriate tools and skills to visualize, interact and explore the data, which of course needs to be coupled with people who are empowered to act on the insights revealed.
Several of the tools demonstrated seemed to use Shiny and R to create interactive graphics. This is a perfect combination of developer skills preparing the right kind of visualizations so that self-service data exploration becomes meaningful. So many self-service reporting tools end up being huge time sinks because the fundamental data organization was incomplete or inconsistent.
One of the featured labs highlighted how expenses were reduced and customer satisfaction increased by the appropriate use of dashboards and educated data exploration. They were able to reduce repetitive testing and quickly identify trends in unreimbursed testing. Then with data at their fingertips they were able to discuss the trade-offs between economic and medical goals.
It makes me cringe when I hear people talk about investing in cryptocurrency.
I have been successfully investing for 25+ years. This involves understanding fundamentals, having a general awareness of the economy while committing to a lot of safe principles like asset allocation, dollar cost averaging, long term perspectives and, most importantly, a disciplined selling strategy.
In short, I am an investor, not a trader.
I view cryptocurrencies as a massive social experiment with the fundamental value propositions still developing. Clearly there is tremendous hype. Beyond the hype, there are stories of people in high-inflation countries exchanging their local fiat currency for crypto as quickly as possible to preserve value. In a geopolitical culture of growing mistrust of governments and institutions, the immutable and pseudonymous transparency of the blockchain, unable to be stopped by a single entity, is compelling.
Bitcoin, presently, is useless as a currency. Why would someone want to spend $30,000 on a car today when that same car could be worth $50,000, or $17,000, tomorrow? Currencies need stability and trust to be useful. Most cryptocurrencies have neither right now.
However, I do like data, big data, AI, and algorithms. The massive price swings, the changes in sentiment, the lack of fundamentals, the 24×7 trading platform and the involvement of tech all make cryptocurrencies compelling to my inner engineer. There is a tremendous amount of raw data available. It all has to be organized and mined.
I decided to programmatically trade in the most reputable cryptocurrencies. The first problem was choosing an exchange, and did I want to hold long positions only, or also short positions and do margin trading?
The reputable cryptocurrency exchanges in the U.S. are all regulated, and, thankfully, that means there is some difficulty in shorting Bitcoin.
Most platforms for margin trading, shorting, and derivative trading of cryptocurrencies are not readily available to U.S. investors. Avatrade, BitMEX, IQ Option and Plus500 appear the be the most reputable, but a foreign bank account is required to trade on these platforms and that’s well beyond the effort level that most people desire.
In the U.S., cryptocurrency margin trading can be done at Bitfinix and LedgerX, though you must be an accredited investor to do so. If you don’t know what that means, you aren’t.
The two safest options for trading cryptocurrency in the U.S. appear to be Coinbase’s GDAX and Kraken. GDAX suspended all margin trading for non-grandfathered accounts in late 2017. Kraken appears to be the only platform in the U.S. for regular people to trade cryptocurrency on margin and has a reputation for excellence along with security, though was plagued with technical problems during Bitcoin’s astronomical December rise (possibly now resolved).
For safety and clear and obvious compliance with U.S regulators, I’ve chosen to stay with GDAX, and forego margin trading until the market stabilizes.
If you are still interested in shorting Bitcoin, check out:
Rob Thomas is General Manager of Analytics at IBM. His main topic was that there is a ladder that must be climbed for the effective use of Machine Learning and Artificial Intelligence. Per Rob, the technology has finally caught up and “2018 is the year to implement Machine Learning.” Part of that is instilling a data-driven culture.
The first rung is that AI is not possible if your data is not simplified and collected into a data lake. Traditional business analytics with a solid data strategy is essentially the prerequisite for a shift into ML. As part of this, the point was made that AI is perceived is being only for hyper-scaling big players like Uber, Airbnb and Facebook but that recent advancements really make it available to all. An insight that really resonated with me was that 80% of the world’s data is sitting unused and neglected behind corporate firewalls.
The second rung is that data must be accessible. Policy and governance must be in place so that data is at the fingertips of the users that need it most. Of course, there are complications with financial data and HIPPA protected data. The idea is to install the data driven culture by making as much data available to anyone who needs it in a self-service manner.
The top rung is built upon traditional analytics and business intelligence—the actual utilization of Machine Learning in every decision. Once data is widely accessible, decisions based on hunches become less viable and people learn to interact with their data using ever more sophisticated tools. This is the destination of all successful companies in the future: data driven decision making.
Rob mentioned the 40/40/20 rule about companies adopting a data driven culture:
Stage 1: 40% are data aware and are building out incremental efficiencies in their data processing, using both proprietary and open-source tools to create their data lakes. Data collection and data cleansing are the current investments.
Stage 2: 40% are actively examining their data looking for new ways to grow. Regardless of their actual industry, they are becoming a technology company.
Stage 3: 20% are exploring Machine Learning with the intention of being data led. It’s the end of opinions. They are evolving to use ML to automate tasks they never wanted to do in the first place.
Vitaly Tsivin of AMC Networks and Alissa Hess of USAA both shared examples of how Machine Learning and data science are being used to transform their companies from the inside out. However, both noted that the biggest barrier to success is finding talent. The combination of understanding data, understanding technology, and understanding business is rare in a person and difficult to hire.
The webinar ended with a presentation by Garry Kasparov, the first chess grand master to be defeated by a computer, and his opinions on the limitations and direction of artificial intelligence.