Tag: a16z

Skydio: The self-flying self-thinking camera has arrived

Skydio makes flying tools that free your hands and mind. Their drone/UAV, the Skydio R1, uses computer vision to map the world as it moves (leveraging 13 cameras to build a 3D map of its surroundings that includes trees, people, buildings, and more), and then uses a 4k camera to record the video you want. You control it with simple to use smartphone apps, but can easily set it to just do its own thing and let it follow you, or your car, while you are on the move. The result is really incredible video.

This is a fantastic use of Artificial Intelligence, Machine Learning and well engineered robotics. We love this!

See it for yourself here:

The video captures some cool use cases, like filming the adventurous exploring the outdoors. But think of the many other use cases this could be used for, like ensuring safety or helping law enforcement or in disaster response. This is really powerful technology.

For more see: https://www.skydio.com/

Skydio on Twitter

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Iceland’s untouched beauty is something dreams are made of. Ride shotgun and see what Danny McGee finds in the depths of the Icelandic wild.

There’s nothing Kendall Martin loves more than discovering new locations and rock-hopping to the ends of the earth — even with an element of danger present.

The AI of the @SkydioHQ R1 is unparalleled. I am not piloting the drone during any of this 6.5 minute flight.

Tap your actor, pick your camera angles in real-time, and focus on performance.

#CinemaAI #computationalCinema

Feel the adrenaline as Dustin winds his way through the woods with nothing but a few inches of rubber under his heels.

The world is what you dream it. Enter the imagination of a 5th grader during her backyard adventures.

Behind the scenes of when @2chainz took R1 for a spin for his show, Most Expensivist. Check out the full episode tonight on @Viceland at 10pm!

Behind the scenes of when @2chainz took R1 for a spin for his show, Most Expensivist. Check out the full episode tonight on @Viceland at 10pm!

Shredders Digest pushing each other to their limits, blazing trails and forming friendships along the way.

They say that true mastery comes from 10,000 hours of practice, if that's the case, then Vanessa is well on her way toward perfection. If at first you don't succeed, try again.... and again... and again!

Riding on the trail with your friends, there's a precious moment where it's all about flowing with the path and seeing where it takes you.

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What The Board Needs To Know About Artificial Intelligence

No matter what the business, Artificial Intelligence (AI) is going to have an impact. It is changing the way consumers consume, changing the way companies create and changing the way industries generate value. It is also improving how givernments function including both service to citizens and national security missions. The bad news is that AI is also being used by criminals and hostile governments. It is a component of the rise of cyberwar and the growing cyberthreat.

C-suite executives and members of boards are rightly concerned with assessing what about AI they should accelerate into their companies and what impact it should have on their firms to beat the competition in delivering value.

Frank Chen of a16z is one of the great visionaries and explainers of artificial intelligence, especially around the AI with potential for real business impact. This video on "Artificial Intelligence: What is working" captures his thoughts on the current golden age of AI (which is really more accurately described at machine learning, deep learning and other distributed computing).

Like every golden age, there is promise and peril to be seen. And there is a need to determine what is hype and what is real. There is also a need to know what is actionable and relevant now and what is coming in the future. All of this is captured by Frank in this succinct video:

This video hits so many hot topics. The bottom line: AI has come roaring out of the research labs and is in business now. Now it is your turn to decide what to do with AI inside your organization.

You can find more insights into the nature of this critical megatrend in the CTOvision section on Artificial Intelligence. And track the most important tech concepts for C-suite executives at The Boardroom.



Andreessen Horowitz: Investing in, and nurturing, great capabilities for the enterprise

Andreessen Horowitz has a model unlike any other Venture Capital firm, and that model is paying off for a broad ecosystem of companies, investors and enterprise IT professionals. From the standpoint of an enterprise technologist, the payoff will come by their support of firms fielding capabilities that will make a positive difference for organizations. The best way to learn about their spirit and approach is to review the blogs of their founders Marc Andreessen and Ben Horowitz. But the best way to prepare yourself for coming disruption, in my opinion, is to dive right into the portfolio of firms they have invested in and are nurturing.

We review a16z firms regularly and summarize firms we believe are most impactful for our readers in our Disruptive Technology Directory. We keep this searchable list up to date and categorize it by functionality to help you find the best solution for your need. We list the categories below.

CTOvision Context and Reporting on a16z

a16z on Twitter

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Generative Adversary Networks: A very exciting development in Artificial Intelligence

For years there has been a growing concern that many forms of machine learning are actually easier to deceive than they should be (and there is good reason to be concerned, for background on why see the paper recommended to me by my friend Lewis Shepherd: "Deep Neural Networks are Easily Fooled").

Many of us have also raised concerns about the current security frameworks around Artificial Intelligence (there are none! The approach to fielding AI is to create capabilities, test them for functionality and field them, with no security frameworks involved). These observations make it important to discuss ways to optimize security of AI along with overall functionality of our systems. Machine learning is becoming ubiquitous now, so we already need ways to improve its ability to perform in the presence of potential adversaries who would seek to deceive models. This is definitely a topic worth discussing and understanding.

In discussions on this topic with Frank Chen of a16z I was very happy to learn that some of the greatest minds in machine learning have been examining this issue. In fact, there is exciting, peer-reviewed research published on the topic and many interesting projects are well underway on methods to address some of these issues.

Perhaps the most exciting domain of research in this area was kicked off by a 2014 research paper titled Generative Adversarial Nets.  It describes ways to use unsupervised machine learning to help systems improve, including improving in environments that include deception.

This paper by Ian Goodfellow and his team at the University of Montreal described Generative Adversarial Nets (GANs) as a way to create two neural network models that fight each other, one creating real results and one creating forgeries. Another model serves as an expert detective that seeks to evaluate all results and know the difference between the fraud and real result.

Goodfellow et al used the metaphor of a counterfeiter seeking to generate fake currency and a detective seeking to tell the difference between real and fake. In their words:

The generative model can be thought of as analogous to a team of counterfeiters, trying to produce fake currency and use it without detection, while the discriminative model is analogous to the police, trying to detect the counterfeit currency. Competition in this game drives both teams to improve their methods until the counterfeits are indistiguishable from the genuine articles.

So, in Goodfellow's model, both the real model and the adversary model will be trained to get better over time, eventually reaching the point where the detectives cannot tell the difference between the real currency and counterfeit. This can be used to continuously improve models.

Where might this research lead? This particular framework is applicable to the field of deep learning, which seeks to discover rich, hierarchical models that represent probability distributions over the kinds of data used in artificial intelligence applications. It is particularly relevant to applications that include natural images, audio waveforms containing speech, and data that contain symbols. But this is early into the research, and it is perfectly appropriate for us to speculate on future use cases of this and other related research.

For example, consider algorithms that seek to automatically detect changes in imagery from satellites and then seek to describe those changes. Was there more or less vegetation in the image? Was the water level higher or lower than the past image? Was there more or less ice or snow? Were there more vehicles? What types were they? Algorithms have been around for these types of problems for years and despite many breakthroughs there is huge need for improvement, especially in those cases where humans might seek to deceive and shape the results. GANs may be key to breakthroughs in how these images are processed.

Another potential area is in computer security. AI, especially machine learning, is being applied to computer security solutions at the endpoint, network and data center in many use cases. It is also making its way into commodity consumer solutions for cyber security. The bad news is that adversaries are also discovering AI and machine learning. The cat and mouse game of cyber attacker vs cyber defender continues. How might the use of GANs help defenders in this domain? One day soon, the AI in commercial cyber security offerings may come with GANs embedded to continuously challenge the system's results and continually seek to improve defenses against increasingly smart adversaries.

These are just a few examples. There are so many others.  GANs will one day be throughout our systems and always on, always seeking to deceive the good AI, and always making AI better.

GANs deserve more focus and we will continue to track them here categorized in our Artificial Intelligence domain. For alerts on future posts on this topic see CTOvision Newsletters.

Subscribe to the a16z Podcast to track the megatrends driving us all forward

My favorite podcast, by far, is the a16z Podcast.  Every edition provides interesting insights on topics technologists and forward thinking executives should track.

The topic I just listed to featured Fei-Fei Li, associate professor at Stanford University, along with the brilliant star of tech trend context Sonal Chokshi and the famous a16z partner Frank Chen. The topic was "When Humanity Meets A.I." and I could not think of a better team to examine the many facets of this interesting topic. From the podcast description:

Who has the advantage in artificial intelligence — big companies, startups, or academia? Perhaps all three, especially as they work together when it comes to fields like this. One thing is clear though: A.I. and deep learning is where it’s at. And that’s why this year’s newly anointed Andreessen Horowitz Distinguished Visiting Professor of Computer Science is Fei-Fei Li [who publishes under Li Fei-Fei], associate professor at Stanford University. Bridging entrepreneurs across academia and industry, we began the a16z Professor-in-Residence program just a couple years ago (most recently with Dan Boneh and beginning with Vijay Pande).

Li is the Director of the Stanford Vision Lab, which focuses on connecting computer vision and human vision; is the Director of the Stanford Artificial Intelligence Lab (SAIL), which was founded in the early 1960s; and directs the new SAIL-Toyota Center for AI Research, which brings together researchers in visual computing, machine learning, robotics, human-computer interactions, intelligent systems, decision making, natural language processing, dynamic modeling, and design to develop “human-centered artificial intelligence” for intelligent vehicles. Li also co-created ImageNet, which forms the basis of the Large Scale Visual Recognition Challenge (ILSVRC) that continually demonstrates drasticadvancesin machine vision accuracy.

So why now for A.I.? Is deep learning “it”… or what comes next? And what happens as A.I. moves from what Li calls its “in vitro phase” to its “in vivo phase”? Beyond ethical considerations — or celebrating only “geekiness” and “nerdiness” — Li argues we need to inject a stronger humanistic thinking element to design and develop algorithms and A.I. that can co-habitate with people and in social (including crowded) spaces.

Download this edition at: When Humanity Meets A.I