2016 is piratically here, and the countdown has begun. What kind of interesting surprises and opportunities will it bring us being the year of the SmartData Monkey?
2016 is going to be an extremely existing year, and that’s not only because I was born on the year of the monkey.
I declare 2016 to be The Year of The Smart Data and Machine Learning!
Above is a data driven conclusion, so let me elaborate…
BigData vs Machine Learning
I like to use google alerts to notify me on the variety of topics of interest as such appear in different publications on the web based on specified (by me) keywords. Now, what if we data mine those alerts for a longer (like a year) duration of time on Big Data vs Machine Learning?
Above is a graphical representation of the Google alerts as such appear in “real-time” for the BigData keywords and Machine Learning keyword (in verity of permutations, i.e. case sensitivity, etc.). In addition to that this graph shows the liner regression of both keyword alerts respectively. While BigData keyword spikes thought a year which I was able to correlate to variety of BigData conferences around the world, Machine Learning hype starts to hit closer to the end of 2015 into 2016 clearly showing the level of interest among consumers as well as the producers of the technology.
Let’s turn to analysts
Great understanding and input is usually well captured in the community of analysts who attempt to not only look at the technology available and emerging, but also the interest in such among the consumer/business community. Gartner represents such relationship in its Hype Cycle.
As you can see Machine Learning has advanced its adoption from 10 years to 5 in one year as well as its progress along the curve from Innovation to Peak of Inflation which is a clear indicator of the uptake and success in the community of business users and use cases.
Back From the Future
While we can talk about self-driving cars, drones, and other emerging technologies that will need Machine Learning and some sort of AI without a doubt, it is great to e that ML finds the solutions in complex problems of the today. Let’s take SIOS iQ as a great example of the Machine Learning application to the challenges of IT operations today.
With broad adoption of virtualization and emerging cloud trends, IT operations becomes a very challenging tasks which calls for a simple and intelligent solution that is capable to learn the behaviors of a particular workload and self-drive IT operations to ensure the efficiency without sacrificing the performance, reliability as well as having visibility into the capacity of the environment which closely related to the budget whether that on-prem or off.
SIOS iQ employs patented Topological Behavior Analysis (TBA) to be able to derive the complex relationships as well as learn the personalized behaviors of the workload that depend on the industry to automatically call out the root cause of the performance, efficiency, reliability or capacity issues as well as to remediation of such. Visit SIOS iQ for more information: http://us.sios.com/iq/
Hot Emerging Companies In ML
Semantic Science is a very successful Machine Learning boutique that was started 7 years ago by a number of Machine Learning veterans with experience building products in space of security, business analytics, speech recognition, bioinformatics, image recognition, and other ML related fields. This is the only “full house Machine Learning” startup that is capable to take or create a vision and turn it into reality through execution which includes strategy, product management, UI/Ux , scalable back-end and algorithm implementations as well as helping organization to build the team to take it forward. Semantic Science is the pioneer of ML consulting and the company to watch in 2016 as I am sure others will follow.
From my recent conversations with the founders, I have gathered that the number of opportunities as well as the size of such that require Machine Learning expertise has grown by 300% which Semantic Science is happy to support by hiring more talented Machine Learning practitioners.
VC investment is yet another indicator that this is not just another hype as money flow into the emerging startups in ML space. Let’s take a look at the acquisitions just from 2015 that will hit the roof in 2016: IBM acquires AlchemyAPI, a deep learning startup; Microsoft buys Revolution Analytics;PayPal buys Paydiant; Dropbox buys CloudOn; Splunk Acquires Caspida, etc.
There are many different projects that exist and existed in BigData and Machine Learning space such as Hadoop, SparkML, Malhout, etc. The one that I would recommend watching in 2015 is OpenAI that has a mission to build open ML community and possibly set of standards in AI space founded by Elon Musk, Sam Altman, and others…
“Everybody and their brother”
Yet, another indicator of AI adoption is when it starts to appeal to many that understand the high-level but not necessarily skilled in art:
Basically, community of technologists are saying that we are ready and we will work on bringing the machine intelligence not only into our labs, but also our homes.
What’s the big deal?
On the personal side, once I have graduated 15 years ago (or so) from my doctorate program in machine learning (not exactly called that way at the time), I have realized that I am not interested in teaching or grants, but I am interested to become a very early Machine Learning practitioners that not only understands the theory but also is capable to deliver the solution which ultimately creates a product that generate revenue (that’s when you really know that AI is real and people need it). While you will likely read a lot of predictions for 2016 that will take place around faster and bigger compute, application of GPUs, etc. I would like to have a different spin on that because considering my journey in this space up to today, I would like to declare 2016 to be the year of Machine Learning, and while it is happening today in small pockets, I am talking about the wide adoption and application to the complex problems of today.
Yes, it is happening now, and will blow up tomorrow (in 2016) as huge opportunities for Machine Learning development, investment, productizian to finally bringing AI to solve the complex problems and support innovation of the future where all the commodity operations are solved by our friendly AI algorithms.