Fueling Cisco’s Innovation Engine with Machine Learning

The partner opportunity to make 1 equal 100

This year, I had the privilege of opening up our Cisco Partner Connection Week conference. As always, I was excited to talk about the future of technology, specifically around machine learning and artificial intelligence (ML/AI), and the opportunity it gives our partners to innovate.

One definition that stands out for me is from Arthur Samuel, who coined the term “machine learning.” He defines it as a “field of study that gives computers the ability to learn without being explicitly programmed.” In other words, a machine learning algorithm can solve problems and improve its skills without direct intervention from a person. There is a broad set of capabilities it can enable, from identifying cancer in medical images, spotting abnormal spending patterns on a credit card, to determining the most optimal path for a self-driving car.

With machine learning we can feed massive amounts of data into the algorithm, then the machine determines the best course of action in the real world (instead of having experts code rules for a machine to follow when they let it operate in the world).

Investment in and adoption of ML/AI is dramatically increasing:

• Venture Capitalists worldwide have poured more than $10B into ML/AI companies over the last year.[1]
• IDC reports that nearly 75% of developers will build AI functionality into their apps this year.[2]
• Gartner predicts that by 2020, AI will be a top five investment priority for more than 30% of CIOs.[3]
• The global machine learning market is expected to grow over 44% by 2022.[4]

 

At Cisco, we are focusing on driving ML/AI capabilities across our portfolio.

Cisco is providing the foundation for digitization, securely enabling the Internet of Things, and bolstering human capability in the era of intelligence through ML/AI. As mentioned above, machine learning is fueled by data. And, Cisco has lots and lots of data. Why does this matter? Because machines learn by seeing lots of versions of something.  For example, to teach a machine to know the difference between a cat and a dog, you need to show it a lot of pictures, with views of cats and dogs from the front, back, side, and above.  With machine learning, the machine with the most “data” on cats and dogs will develop the best way to tell the difference on its own. The broader the data set, the better!

Cisco has a unique and broad understanding of data. That gives us the opportunity to fully unleash the power of the network, gain actionable insights, protect our customers, and accelerate innovation. If we look across the priorities we are driving to deliver the secure intelligent platform for digital business – ML/AI technologies are already playing a critical role.I highlighted these technologies in the context of the pillars above at Partner Connection Week:

• Cisco Encrypted Traffic Analytics (ETA) uses machine learning trained on network data on a massive scale to find malware inside encrypted traffic – without decrypting it. Watch my favorite ETA video with comedian Aasif Mandvi.
• Cisco DNA Analytics pulls data from customers’ “opt-in” network telemetry, securely anonymizes it, aggregates it, learns patterns in it, and dynamically applies the learnings and insights across all of our customers.
• Cisco Intersight uses predictive analytics and machine learning to synthesize data and provide actionable intelligence.
• Machine learning has been part of AppDynamics’ fabric since the beginning, but Business iQ expands its capabilities with automated root cause analysis while broadening its data model with new IoT agent and network visibility.
• Cisco WebEx Assistant is an enterprise-ready AI-powered voice assistant that uses natural language processing to understand voice signals, freeing customers from having to enter a string of numbers and letters to “dial in” to a meeting.

I also had the opportunity to demo an early stage innovation developed by key members of Cisco Cortex, an elite internal ML/AI Forum, with “Cisco Demo Guy,” Sean Curtis!

We showcased a demo of a reasoning engine doing real-time reasoning of live video. This technology goes beyond the ability to simply identify something, to the ability to explain why something happened.

We used an example from a live pilot in a large European city that is using video analytics to make the city safer by protecting pedestrians in crosswalks. The reasoning engine recognizes activities, categorizes them, and understands interdependencies between other variables such as traffic light rules or time of day, among others. Then, it gives insight on how to make improvements for pedestrian safety. The same reasoning engine can be used for many more use cases. Stay tuned for more on this innovation – it will be showcased again at Cisco Live in Orlando.

For every one innovation from Cisco, partners have the opportunity to create 100!

The pace of innovation coming from Cisco and from our partners is accelerating exponentially.  And, ML/AI is partners’ green field opportunity to innovate even more. Cisco is helping partners accelerate their practices with:

• Programs and training for next generation skills through the Cisco Learning Network and the DevNet Developer Program
• Co-development opportunities across our partner ecosystem
• Next generation platforms – networking, cloud, collaboration – where our partners can deliver more innovation.

At PCW, I proposed a new normal where one innovation no longer equals one additional innovation, because with every single innovation Cisco launches, partners can create even more innovation on top of that. We want partners to hit 1=100 or even 1=1000!  This is Cisco and partners innovating together exponentially and making a true impact for our customers.

Cisco Partners can view the PCW opening keynote on Cisco SalesConnect.

 

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[1] PitchBook: 2017 Year in Review: The top VC rounds & investors in AI, Dana Olsen, December 20, 2017

[2] IDC FutureScapes 2018

[3] Gartner Says AI Technologies Will Be in Almost Every New Software Product by 2020, July 18, 2017

[4] Forbes: Roundup of Machine Learning Forecasts and Market Estimates, 2018, Louis Columbus, February 18, 2018