Summary: There are some interesting use cases where combining CNNs and RNN/LSTMs seems to make sense and a number of researchers pursuing this. However, the latest trends in CNNs may make this obsolete.
Summary: Our recent series of articles on AI strategies shows the options available for the strategic direction of your AI-first company. Here are some thoughts on moving from strategy to implementation, including some useful tools to help in planning.
Added by William Vorhies on August 7, 2018 at 7:25am — No Comments
Summary: Now that we’ve detailed the four main AI-first strategies: Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it’s time to pick. Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup.
Added by William Vorhies on July 31, 2018 at 8:20am — No Comments
Summary: The fourth and final AI strategy we’ll review is Systems of Intelligence (SOI). This is getting nearly as much attention as the Vertical strategy we previously reviewed. It’s appealing because it seems to offer the financial advantages of a Horizontal strategy but its ability to create a defensible moat requires some fine tuning.
Added by William Vorhies on July 24, 2018 at 9:00am — No Comments
Summary: Getting an AI startup to scale for an IPO is currently elusive. Several different strategies are being discussed around the industry and here we talk about the horizontal strategy and the increasingly favored vertical strategy.
Added by William Vorhies on July 17, 2018 at 7:00am — No Comments
Summary: A defensible data strategy increasingly defines those AI businesses that will be successful. VCs know this and are steering the funding to this strategy. Read here about what a defensible data strategy is and how to identify your next AI opportunity using this technique.
Added by William Vorhies on July 10, 2018 at 7:00am — No Comments
Summary: As advanced analytics and data science have matured into must-have skills, data science groups within large companies have themselves become much larger. This has led to some unique problems and solutions that you’ll want to consider as your own DS group grows larger.
Summary: Computational Synthetic Biology (CSB) is likely to be both the next big thing and perhaps most important field to exploit data science. As the name implies, this lies at the intersection of data science and biological research. Big advancements and big investments are already starting to occur here. Data scientists with deep learning skills will want to check this out.
Added by William Vorhies on June 19, 2018 at 7:55am — No Comments
Summary: Data Science is the secret sauce that turns the dumb internet into the smart internet driving changes in society as fast as we drive changes in the internet. The best place to find data on this is Mary Meeker’s Internet Trend Report 2018. Here we use that data to look back at the last year and forward a bit in time to see what impact data science is having.
Added by William Vorhies on June 12, 2018 at 8:30am — No Comments
Not quite ready for world domination! 'Suicidal' security robot drowns itself in a fountain
A security robot created by the company Knightscope was patrolling an…Continue
Summary: There is a great hue and cry about the danger of bias in our predictive models when applied to high significance events like who gets a loan, insurance, a good school assignment, or bail. It’s not as simple as it seems and here we try to take a more nuanced look. The result is not as threatening as many headlines make it seem.
Added by William Vorhies on June 5, 2018 at 8:00am — No Comments
Summary: Researchers in Synthetic Neuro Biology are proposing to solve the AGI problem by building a brain in the laboratory. This is not science fiction. They are virtually at the door of this capability. Increasingly these researchers are presenting at major AGI conferences. Their argument is compelling.
Added by William Vorhies on May 21, 2018 at 3:00pm — No Comments
Summary: The annual Burtch Works study of Data Science salaries and employment statistics is just out and things continue to look great. There are some interesting trends here you’ll want to know about.
Summary: Before starting to develop an AI strategy, make sure your team understands the limits of what is reasonable today, as well as incremental improvements that might be overlooked. Focus should be on your LOB leaders who understand the business. Make sure they are also able to recognize AI opportunities.
Added by William Vorhies on May 8, 2018 at 9:30am — No Comments
Summary: Our starting assumption that sequence problems (language, speech, and others) are the natural domain of RNNs is being challenged. Temporal Convolutional Nets (TCNs) which are our workhorse CNNs with a few new features are outperforming RNNs on major applications today. Looks like RNNs may well be history.
Summary: Not everyone wants to invest the time and money to become a data scientist, and if you’re mid-career the barriers are even higher. If you still want to be deeply involved in the new data-driven economy and well paid, the growth rate and opportunities as a data engineer or business analyst need to be on your radar screen.
Summary: Deep Learning, based on deep neural nets is launching a thousand ventures but leaving tens of thousands behind. Transfer Learning (TL), a method of reusing previously trained deep neural nets promises to make these applications available to everyone, even those with very little labeled data.
Added by William Vorhies on April 17, 2018 at 12:25pm — No Comments
Summary: There are several things holding back our use of deep learning methods and chief among them is that they are complicated and hard. Now there are three platforms that offer Automated Deep Learning (ADL) so simple that almost anyone can do it.
Summary: Automated Machine Learning has only been around for a little over two years and already there are over 20 providers in this space. However, a new European AML platform called Tazi, new in the US, is showing what the next generation of AML will look like.
Added by William Vorhies on April 3, 2018 at 7:30am — No Comments
Summary: GDPR carries many new data and privacy requirements including a “right to explanation”. On the surface this appears to be similar to US rules for regulated industries. We examine why this is actually a penalty and not a benefit for the individual and offer some insight into the actual wording of the GDPR regulation which also offers some relief.