BootstrapLabs at The World’s Largest Entrepreneurship Conference

EVENT:  TiEcon 2019 May 10th and 11th,  Santa Clara Convention Center

Nicolai Wadstrom, Founder and CEO of BootstrapLabs, will be interviewed live on stage at TiEcon 2019. Nicolai will share his story and explain how BootstrapLabs has become a leading Venture Capital firm in Applied Artificial Intelligence.

TiEcon is the largest technology anchored conference dedicated to fostering entrepreneurship. It has attracted over 60,000 entrepreneurs and professionals from over 50 countries in the past. Last year alone, there were over 5,000 participants from 19 countries.

TiEcon 2019 will focus on the hottest areas of innovation including AI/machine learning, security, FinTech, and digital health, in addition to hosting its flagship tracks on “entrepreneurship how-tos”, youth, and women.

Nicolai will be interviewed during a FIRESIDE CHAT on Friday, May 10th, from 3:00 to 3:30 pm. Join him and learn how BootstrapLabs selects, invests and supports the best Applied AI entrepreneurs.

This year, along with Nicolai, many other industry leaders will join the conference including Eric S. Yuan,  CEO of Zoom, Ashish Bansal, AI Leader, ML Engineering at Twitter, and many C-level executives from companies such as Adobe, NVIDIA, Salesforce, McKinsey, FedEX, Oracle, IBM, and many others.

To sign up for our mailing list, click here.

BootstrapLabs To Speak at The SuperReturn US West Private Equity Venture Capital Conference, Feb 11-13

BootstrapLabs Founder & CEO, Nicolai Wadstrom, and Co-Founder, Benjamin Levy, will be speaking at the SuperReturn US West Private Equity Venture Capital Conference in Los Angeles on February 11th and 13th, 2019.

The conference will bring together over 300 of the leading players in private equity and venture capital from the West Coast, for an unparalleled opportunity to meet, learn and create new business ventures.

Nicolai and Ben, along with other leading industry experts will share their strategies and insights, and discuss the trends, opportunities and challenges facing the future of venture capital.

If you’re in the area, come see BootstrapLabs participate in this influential conference. For a 10% discount, purchase your ticket here and enter following code:

FKR2488EMSPK

BootstrapLabs will be hosting a reception in a private villa in the Beverly Glen area of Los Angeles on Monday, February 11th, from 6:30 to 9:00 pm.

We are inviting family offices, institutional investors, and other limited partners who are interested in decoding the AI trends, their impact on existing portfolio allocations, and capture what promises to be one of the largest wealth creation opportunities of the next decade.

If you are interested in joining please contact events@bootstraplabs.com.

Agenda Featuring BootstrapLabs

Feb 11, 09:20 – 10:00

Benjamin Levy will be participating onstage as a panelist. The discussion will cover the following topics.

Title: Future gazing: how will the VC landscape change by 2030?

  • How do you spot the superstar of the future?
  • Where is Venture going and how is it evolving?
  • Is disruption likely to keep going?
  • Is Venture likely to cease in a recession?
  • How are VC strategies developing? What sectors are VC moving into?
  • Are there new ways to finance portfolio companies?
  • What other hybrid and innovative models are there in Venture?

Additional Panelists

  • Jeff Grabow, US VC Leader at EY
  • Xinhong Lim, Director at Vickers Venture Partners
  • Carmen Palafox, Partner at Make in LA

Feb 13, 15:40 – 17:20

Nicolai Wadstrom will be participating onstage as a panelist. The discussion will cover the following topics.

Title: Technology spotlight – Rotating roundtable presentations and Q&A

Examining the most influential technologies; what they are, what makes them important, and how to invest – 10 minutes on each topic then delegates change table.

  • Automation – How does it work, and how do I invest?
  • Artificial Intelligence & Hardware – How can it be used, and how do I invest?
  • Blockchain – How does it work, and how do I invest?
  • Cryptocurrency – How does it work, and how do I invest?

Additional Panelists

  • Peter Liu Vice, President at Pritzker Group
  • Aymerik Renard, General Partner at Hardware Club
  • Venktesh Shukla, General Partner at Monta Vista Capital

For more information please visit the conference website.

BootstrapLabs - BloombergNEF IMAGE

BootstrapLabs on Bloomberg News

In December 2018, our CEO and Founder, Nicolai Wadstrom, met with Danya Liu, Emerging Technology Analyst at Bloomberg NEF in San Francisco. He shared some details about the BootstrapLabs investment strategy, and his vision on how Artificial Intelligence will impact our world.

Each month Bloomberg features a strategic investor active in one of the key industries they track. Their profiles provide an outline of the main investors, portfolio companies, characteristics, and strategies involved in the company, as well as their own opinion on what makes their operation so interesting.

We are pleased to share with our community the first Bloomberg Tech Radar report of 2019,  featuring BootstrapLabs. You can access a copy of the report (courtesy of BloombergNEF) by clicking on this link or the image below, and entering your email address. 

(courtesy of BloombergNEF)

 

BootstrapLabs - Ferrari Image

Powering Autonomous Dreams

Authors

Thomas A. Campbell, PhD – Founder & President, FutureGrasp™, LLC[1] and Special Advisor, BootstrapLabs
Nicolai Wadstrom – CEO and Founder, BootstrapLabs

The Dream

Driving used to be a pleasure: the open road, Sunday drives, cruising on Main Street. Now it is more of a chore: traffic jams, road rage, avoiding distracted drivers. While in the last few years we have advanced with capabilities such as Waze and Google Maps in our unending quest to dodge traffic and still make it home in time for a hot dinner, the thrill of being in the driver’s seat is waning.

Moreover, there are few things that we spend more time doing than commuting. “New survey data show the average American’s commute inched up to 26.9 minutes from 26.6 minutes the previous year [based on a one-way commute], according to the U.S. Census Bureau’s 2017 American Community Survey.”[2] US cities with the worst traffic have more than 40 minute one-way commutes.[3] Thus, many commuters spend more than an hour of every workday just driving; our time is being consumed by this stressful and unproductive activity.

Finally, polluting cars are a significant source of carbon dioxide (CO2) emissions, a major contributor to climate change.[4] “EIA [Energy Information Administration] estimates that U.S. motor gasoline and diesel (distillate) fuel consumption for transportation in 2016 resulted in the emission of about 1,102 million metric tons of CO2 and 437 million metric tons of CO2, respectively, for a total of 1,540 million metric tons of CO2. This total was equal to 82% of total U.S. transportation sector CO2 emissions and equal to 30% of total U.S. energy-related CO2 emissions in 2016.”[5] Clearly, this is unsustainable.

So what might we do to realize the dual dreams of hassle-free, environmentally-friendly driving? One approach is to make our vehicles autonomous…to empower the car to do the thinking instead of the driver, and thus free us to do things other than focus on that next stop sign. Moreover, making autonomous vehicles (AVs) become electric vehicles (EVs) could be an even further victory for our environment.[6] This vision is being aggressively pursued by many companies and researchers; unfortunately, reality imposes several technical challenges.

Reality & Challenges

Autonomous vehicles have been considered possible with the right technology aids almost since the first mass assembly car was manufactured.[7] However, only in the last decade have AVs been taken seriously with recent advances in computation and sensors. Perhaps starting with Google’s initially secret Waymo project – itself stemming from Larry Page’s undergraduate vision of being chauffeured around Stanford University campus autonomously – AVs have started to gain traction.[8] Nevertheless, there remain many fundamental challenges, both technical- and policy-related, before we can take our eyes off the road. The one we’d like to focus upon in this note is the need for power.

A few years ago, the only EV many Americans had ever seen was a golf cart.[9] Surprisingly, several of the first automobiles were all-electric in the late 1800s. Popular in cities (where one could get electricity), EVs fell out of favor with the introduction of the mass-produced Ford Model T car, followed by the discovery in the United States of cheap Texas crude oil. EVs all but disappeared by 1935.[10]

With advances in technology – especially batteries that offer range up to 300 miles on one charge – EVs have experienced a resurgence in popularity. “Today, there are 23 plug-in electric and 36 hybrid models available in a variety sizes.”[10] The United States just exceeded one million EVs on the road.[11] [12] One of the leading companies developing EVs is Elon Musk’s Tesla. The Tesla Model 3 and its other brands are wonders of technology.[13] However, despite full touch-screen for nearly all its controls and many other technology perks,[14] Tesla cars would be just hunks of metal without power, as well as the computers for autonomous controls.

Power (Batteries vs. Fuel Cells)

Batteries have come a long way since Nikola Tesla himself did fundamental research on them in the late 1800s. Current go-to materials for EV power storage batteries include exotic compounds and elements such as nickel-cadmium, nickel-metal hydride, and lithium. “Lithium-ion (Li-ion) and Lithium-Iron-Phosphate (LiFePO4) cells have so far provided the biggest boost in energy density, between 300% and 400% more energy-dense than LA [lead-acid] batteries. Nissan Leaf, all Tesla vehicles, and most other electric cars and hybrid electric cars use Li-ion battery packs, though a few hybrid and electric car startups are toying with LiFePO4 chemistry.”[15]

Despite the popularity of Li-ion batteries, a lingering technical challenge is their long charge time and power required for each charge. A full-day charge for a Tesla Li-ion battery pack takes several hours and consumes about 2,500 Watts.[16] People who don’t have access to a charger at work typically charge their Tesla batteries overnight at home. Charging EVs puts a new strain on electrical grids.

Another option for powering EVs is the fuel cell, which has been around for decades.[17] “Like a battery, a fuel cell harnesses a chemical reaction to produce energy in the form of electricity. More specifically, hydrogen fuel cells generate electricity, water and heat from hydrogen and oxygen.”[18] A fundamental issue with hydrogen fuel cells is the storage of the hydrogen in the car before its use; one wants to have as much hydrogen as possible on-board in some compressed form to ensure long mileage. Simple hydrogen liquefication doesn’t go far enough; one must resort to trapping the H2 molecules inside or with other solid materials and then enabling their easy release.[19] Nanomaterials encapsulating or bonded to H2 have been researched for several years to do that.[20] Carbon buckyballs, nanotubes and other exotic molecular shapes have all been considered for their ability to store H2 and release it when needed. e.g., [21][22][23]

Despite their comparative environmental friendliness (hydrogen fuel cells emit only water vapor and warm air), fuel cells still have a ways to go before they can, if ever, usurp the efficiency and cost-effectiveness of batteries. “Hydrogen fuel cells offer a potentially very clean, energy dense and easy to recharge energy source for vehicles and other systems, but they are currently complicated, expensive and dangerous to operate. In comparison, lithium-ion batteries, although less energy dense and slower to recharge, are as clean, much cheaper, easier and safer to handle.”[18] Although a few models of automobile EVs powered by hydrogen fuel cells have been manufactured, they have not received wide adoption. Some fleets of buses, as well as forklifts, use hydrogen fuel cells. Wider adoption will require not only major advances in H2 storage capabilities, but also a substantially expanded fleet of charging stations dispersed across the United States.[24] For the next several years, we will most probably see hydrogen fuel cells in only limited use within EVs.

Sensors and Computation

One doesn’t realize it after some experience, but the action of driving actually requires substantial attention and skill. Distracted driving (whether via texting, taking a phone call, or drunk driving) is well-documented to cause accidents.[25] Clearly, computers don’t have the social or biological needs that humans do, but ultimately AV systems still have several significant technical specifications for sensors and computation – a few examples include:[26]

  • High-resolution, 3D maps. When driving, one needs to know where one is going. While humans can look at maps and easily adjust to changing driving conditions, such actions are not trivial for an AV. “Self-driving cars currently lack the common sense needed to navigate using a traditional human map. Since they can’t interpret context, they need to rely on a map signal that doesn’t cut out in tunnels, waver in precision or fall out of date…these machine maps must meet several key demands:
    • Incredible precision, so the car can compensate for its lack of understanding context and know where it is within 10 cm.
    • Granular instructions, like which lane the car is in, the traffic rules that apply to that lane, and even overhead clearances and road elevation.
    • Constant connection, which continues to provide information even when GPS signals are weak or missing.”[27]
    • Advanced sensors systems – increasingly prototypes incorporate LiDAR, Radar and other sensor systems that compensate for the AV systems lack of ability in some areas, but also add capabilities beyond human drivers. BootstrapLabs’ portfolio company AEye is an example of such a technology.[28]
  • Data processing. AVs place incredible demands on the volume of data needed to be processed rapidly. 5G or faster wireless speeds are said to be required. “When it comes to autonomous vehicles, the speeds and data processing capabilities needed to mimic the timing of human reflexes are incredible. Dr. Joy Laskar, co-founder and CTO of Maja Systems, believes the future self-driving car will generate approximately two petabits of data—the equivalent of two-million gigabits.”[29]
  • Object identification. Is that a bicycle or a deer ahead? While easy for a human to discern objects, AVs have troubles. One of the grand challenges with AVs is that objects on the road are in motion—by themselves and/or relative to the AV itself. Thus, AVs must not only process static images, but also moving video data. Some researchers are resorting to infrared cameras to ensure object visualization and identification, even in the worst conditions.[30]
  • Varying weather conditions. Whether you live in the mountains or on a beach, the weather can change quickly. Sensors must be able to accommodate the full spectrum of ambient conditions, including sun, rain, hail, snow, fog, ice and winds. Unfortunately, designing sensing capabilities that cover the full meteorological spectrum is challenging. “After years of testing, with hundreds of cars and vans deployed on public streets and private facilities, even the best autonomous-driving efforts still struggle with inclement weather…At the moment, autonomous cars rely on a patchwork of sensors: GPS, traditional cameras, radar, and lidar technology that bounces lasers off nearby cars and pedestrians. Mother Nature essentially sidelines two of those four applications: cameras are useless in fog and heavy snow, and lidar lasers careen wildly off raindrops and snowflakes. The remaining systems also have major deficiencies. GPS connections can be slow and spotty, and radar is lousy at distinguishing obstacles—is that a pedestrian or a seagull?”[31] New technologies and software are needed to ensure our trips aren’t limited by changing weather.

Opportunities for Applied Artificial Intelligence (AI)

The various challenges described above present unique opportunities, as well as the basic necessity, for Applied AI. As noted in a previous note from BootstrapLabs and FutureGrasp™, LLC,[32] AI can be leveraged positively for energy utilities. Similar thoughts apply in the context of EVs. Let’s review briefly how Applied AI can help us realize the dream of electrically powering AVs:

Advanced materials research. Over a hundred years ago, Thomas Alva Edison used a purely empirical approach to narrow his choice of a filament material for the first sustained light bulb. Later as computers became available, the ability to not only record data, but also to simulate how a material would behave with a given set of material properties and desired results became possible. Now with Applied AI we can take modeling advanced materials to a new level. Companies such as Citrine Informatics leverage AI and big data analytics to go way beyond the Edisonian approach. “Our platform ingests and analyzes vast quantities of technical data on materials, chemicals, and devices to streamline R&D, manufacturing, and supply chain operations for any organization that produces a physical product.”[33] Such AI-driven research can accelerate the development of new batteries and fuel cells. Instead of empirically assessing millions of compounds or spending countless hours programming and running simulations, AI can cut the time and budget spent on finding new advanced materials. Companies such as Toyota[34] and Tawaki,[35] as well as US Government efforts such as DARPA’s Make-It program[36] are using AI to tease out from Mother Nature new synthetic molecules that could be used in next-generation battery and fuel cell technologies.

Optimizing AV Performance. The aforementioned set of sensing and computation challenges for AVs all have two major issues in common: data and its processing. The sheer amount of data that a single AV accumulates on the road is astounding. “A single autonomous test vehicle produces about 30 TB per day, which is 3,000 times the scope of Twitter’s daily data.”[37] This volume of data overwhelms CPUs[38] or other lesser chips. On-edge computing (where the compute is done at the site of data generation) is also paramount as one would certainly prefer not to have loss of WiFi result in a sudden loss of computation and AV control. AI can assist with both these issues. Large data processing using AI with on-board GPU[39] packs is becoming now the go-to solution for AVs.[40][41]

Fulfilling the Dream

Autonomous vehicles powered by electricity require that AI move out of university and industry labs and into the real world—that it is actually “applied.” One means of making that happen is through the creation of start-ups that have a vision of changing the world. BootstrapLabs, a venture capital group in San Francisco, recognizes that challenge and invests in founders that dream big and are solving today’s hardest problems by applying AI to shape a better future.

Across the spectrum of challenges within AVs and EVs, applied AI has the potential to help resolve thorny problems, whether they be in discovering the next-generation advanced material for batteries or fuel cells, or in optimizing datasets and their processing. Achieving the dream of powering AVs will require capabilities beyond what a human or even big data analytics can accomplish. Ultimately, AI can help us make driving a pleasure again.


Notes (all web links accessed November 2018)

[1] FutureGrasp™, LLC, https://www.futuregrasp.com/

[2] G. Salvidia, “Stuck In Traffic? You’re Not Alone. New Data Show American Commute Times Are Longer,” September 20, 2018, NPR, https://www.npr.org/2018/09/20/650061560/stuck-in-traffic-youre-not-alone-new-data-show-american-commute-times-are-longer

[3] “These 25 cities have the worst commutes in America,” October 16, 2018, https://www.nwitimes.com/jobs/these-cities-have-the-worst-commutes-in-america/collection_2745a1a9-6757-59f3-a864-fd4a44cd212a.html#3

[4] Intergovernmental Panel on Climate Change, “Global Warming of 1.5°C,” http://www.ipcc.ch/report/sr15/

[5] U.S. Energy Information Association, Frequently Asked Questions, https://www.eia.gov/tools/faqs/faq.php?id=307&t=10

[6] Of course, a debate rages about whether EVs are actually more environmental than cars with combustion engines. Electricity doesn’t just magically appear – it has to come from somewhere, and those energy sources themselves may be big polluters. One must crawl down the whole electron chain to learn whether the local energy utility is primarily burning fossil fuels or using renewable energy sources.

[7] L. Dormehl, S. Edelstein, October 28, 2018, “Sit back, relax, and enjoy a ride through the history of self-driving cars,” Digital Trends, https://www.digitaltrends.com/cars/history-of-self-driving-cars-milestones/

[8] No pun intended

[9] Golf carts typically have speed-limit governors on them, as one author [TAC] experienced when he caddied in his teenage years. Some friends of his learned the hard way that disabling the governor still doesn’t give a cart enough power to jump a creek.

[10] “The History of the Electric Car,” September 15, 2014, Department of Energy, https://www.energy.gov/articles/history-electric-car

[11] M. Joselow, “The U.S. Has 1 Million Electric Vehicles, but Does It Matter?,” October 12, 2018, https://www.scientificamerican.com/article/the-u-s-has-1-million-electric-vehicles-but-does-it-matter/?utm_source=newsletter&utm_medium=email&utm_campaign=tech&utm_content=link&utm_term=2018-10-23_more-stories&spMailingID=57626706&spUserID=MTExNTAyMjAzODk1S0&spJobID=1503280707&spReportId=MTUwMzI4MDcwNwS2

[12] Although one million vehicles still only represents less than one percent of all wheels on roads in the United States

[13] Tesla, https://www.tesla.com/

[14] Including its 2017 Easter Egg of a software upgrade: ‘Ludicrous Mode,’ providing 0-60 mph in under 2.3 seconds

[15] Chilton, “A Short History of Electric Car Batteries,” http://blog.chiltondiymanuals.com/short-history-electric-car-batteries/

[16] Energy Sage, “Tesla Model S and Model X charging: everything you need to know,” March 27, 2018, https://www.energysage.com/electric-vehicles/charging-your-ev/charging-a-tesla/

[17] One author [TAC] saw a prototype hydrogen fuel cell car at General Motors (GM) in 1990 during his mechanical engineering summer internship there. Sadly, the vehicle never made it off the research floor.

[18] E. Wertheimer, “Hydrogen Fuel Cells vs Lithium-ion Batteries in Electric Vehicles,” June 20, 2018, Furo Systems, https://www.furosystems.com/news/hydrogen-fuel-cells-vs-lithium-ion-batteries-in-electric-vehicles/

[19] Department of Energy, “Hydrogen Storage – Basics,” https://www.energy.gov/eere/fuelcells/hydrogen-storage-basics-0

[20] “Basic Research Needs for the Hydrogen Economy,” May 13-15, 2003, https://science.energy.gov/~/media/bes/pdf/reports/files/Basic_Research_Needs_for_the_Hydrogen_Economy_rpt.pdf

[21] E. Erünal, F. Ulusal, M.Y. Aslan, B. Güzel, D. Üner, “Enhancement of hydrogen storage capacity of multi-walled carbon nanotubes with palladium doping prepared through supercritical CO2 deposition method,” International Journal of Hydrogen Energy, 43 (23), June 7, 2018, 10755-10764, https://www.sciencedirect.com/science/article/pii/S0360319917346967

[22] T. Liu, “Buckyball’s Hydrogen Spillover Effect at Ambient Temperature Observed Experimentally for the First Time,” March 26, 2018, Chemical Communications Blog, http://blogs.rsc.org/cc/2018/03/26/buckyballs-hydrogen-spillover-effect-at-ambient-temperature-observed-experimentally-for-the-first-time/

[23] Y. Liu, et al., “Metal-assisted hydrogen storage on Pt-decorated single-walled carbon nanohorns,” 50 (13), November 2012, 4953-4964, https://www.sciencedirect.com/science/article/pii/S0008622312005349

[24] Wikipedia, “Fuel Cell Vehicle: Buses,” https://en.wikipedia.org/wiki/Fuel_cell_vehicle#Buses

[25] National Safety Council, “Distracted Driving Research, Infographics,” https://www.nsc.org/road-safety/safety-topics/distracted-driving/research

[26] A. Marshall, “After Peak Hype, Self-Driving Cars Enter The Trough Of Disillusionment,” December 29, 2017, WIRED, https://www.wired.com/story/self-driving-cars-challenges/

[27] N. Garg, “Self-driving cars need a new kind of map,” https://www.axios.com/self-driving-cars-need-a-new-kind-of-map-df0b7e69-e0fb-4a8e-a865-24bc96708103.html

[28] AEYE, https://www.aeye.ai/

[29] B. Khosravi, “Autonomous Cars Won’t Work – Until We Have 5G,” March 25, 2018, Forbes, https://www.forbes.com/sites/bijankhosravi/2018/03/25/autonomous-cars-wont-work-until-we-have-5g/#614ff971437e

[30] N. Pinon, “Next-generation technology is coming to a self-driving car near you,” November 7, 2018, PhysOrg, https://phys.org/news/2018-11-next-generation-technology-self-driving-car.html

[31] K. Stock, “Self-Driving Cars Can Handle Neither Rain nor Sleet nor Snow,” September 17, 2018, Bloomberg Businessweek, https://www.bloomberg.com/news/articles/2018-09-17/self-driving-cars-still-can-t-handle-bad-weather

[32] T. Campbell, N. Wadstrom, (November 26, 2018), “Banish the Darkness with Artificial Intelligence,” BootstrapLabs Blog, https://bootstraplabs.com/blog/2018/11/26/banish-the-darkness-with-artificial-intelligence/

[33] Citrine Informatics, https://citrine.io/

[34] M. Moon, “Toyota is using AI to hunt for new battery materials,” March 30, 2017, https://www.engadget.com/2017/03/30/toyota-research-ai-battery-material-hunt/

[35] Tawaki, “What If Artificial Intelligence Finds New Battery Materials,” June 1, 2017, http://www.tawaki-battery.com/artificial-intelligence-new-battery-materials/

[36] Make It, DARPA, https://www.darpa.mil/program/make-it

[37] J.A. Amend, “Storage Almost Full: Driverless Cars Create Data Crunch,” January 17, 2018, https://www.wardsauto.com/technology/storage-almost-full-driverless-cars-create-data-crunch

[38] Central processing units

[39] Graphics processing units

[40] See for example, NVIDIA’s overview of GPUs in AVs: https://www.nvidia.com/en-us/self-driving-cars/drive-platform/

[41] For an overview of advanced semiconductor technologies relative to AI see: T. Campbell & R. Meagley, Next-Generation Compute Architectures Enabling Artificial Intelligence – Part I of II, 2 FEB 2018, https://www.futuregrasp.com/next-generation-compute-architectures-enabling-artificial-intelligence-part-I-of-II and T. Campbell & R. Meagley, Next-Generation Compute Architectures Enabling Artificial Intelligence – Part II of II, 8 FEB 2018, https://www.futuregrasp.com/next-generation-compute-architectures-enabling-artificial-intelligence-part-II-of-II

Banish the Darkness with Artificial Intelligence

The Need

Most everything we do in life requires electricity. Transportation, farming, homes, businesses, computing—they all necessitate the efficient movement of electrons from a power production source to an energy use site¹. Unfortunately, since Thomas Alva Edison first illuminated a Menlo Park street in 1879 – thus banishing the darkness 2 – the  demands humanity has placed upon energy utilities have skyrocketed and consequently at least parts of our energy grid have become brittle and prone to failure. Moreover, the complexity of operating local, regional and national systems that are susceptible to cyber-attacks has made electricity a critical topic for national security.

World population has quintupled since the first sustained artificial light in the late 1800s. With more than 7.6 billion people now teeming on our planet and all of us needing increasing amounts of energy 4, we cannot rely anymore on simple dynamo generators creating electricity and sending it straight to the desired location. On the contrary, our electricity grids are highly complex with numerous power sources (coal, gas, oil, nuclear, and renewables—biomass, solar, wind); switching and amplifying stations; transformers; above and below ground wiring; and storage batteries. Unsurprisingly, even considering conservative trends, our energy use is growing rapidly:

“In the New Policies Scenario, global energy needs rise more slowly than in the past but still expand by 30% between today and 2040. This is the equivalent of adding another China and India to today’s global demand.” 5

Moreover, our already highly electrified society will become even more dependent on charged electrons with new technologies. The Internet of Things (IoT), which requires cloud and edge computing at massive scale, is forecasted to exceed 75 billion devices connected to the internet by 2025 (up from 23 billion presently).6 Research into autonomous vehicles is rapidly approaching deployment stages. Testing underway in Phoenix, AZ and Pittsburgh, PA may be halted temporarily because of bad algorithms causing accidents or irregular policies, but other countries are not waiting around for the United States to perfect driverless cars. China is building cities specifically designed for autonomous vehicles.The Hyperloop – Elon Musk’s dream of accelerating people and cargo in a vacuum tube to near the speed of sound – is being tested in the United States and Europe.8

Population growth is also spurring construction of whole cities from scratch; examples abound in Asia. China plans on constructing a new city the size of Chicago (2.7 million people) with the latest technologies, and thus high energy demands.5 With support from Singapore, the Indian state Andhra Pradesh is building a new capital. “It will be a stupendous 7,235 square kilometres, 10 times the size of Singapore’s own 716 square kilometres.”9

The Risks

With so much riding on our need for energy, we must avoid costly failures. For example, when one author [TAC] worked in the semiconductor industry, the great Northeast Blackout of 2003 was tripped only a few miles from the production facility where he worked. The source of the power failure was overgrown tree branches that had fallen on a transformer. Normally, this would have remained a local issue, but the lead utility failed to react quickly enough to disengage the switches to other grids, thus causing a cascading, multi-grid failure for 50 million people from Ohio to New York to southeast Canada. People were trapped in subway cars and elevators for hours; cell phone service was disrupted for millions. 10 11

Perhaps even more dangerous than a lack of sensors on the grid and failed switches is the looming possibility of a cyber-attack targeting fragile points in the electrical system. Industry is spending billions in its attempts to strengthen cybersecurity of the brittle grid.12 “U.S. utilities will spend a cumulative $7.25 billion in security from now until 2020, with distribution automation assets as the core focus.”13 Grid security is a major issue within the US Government; simulations of cyber-attacks are routinely done by groups such as DOE and DHS.14 15

While a completely failure-proof grid may be impossible with the moving demand target caused by new technologies online and rapidly growing electricity demands, companies and government agencies must nevertheless do their best to anticipate and to prevent both accidental and nefarious situations before they happen. This presents a unique opportunity to engage newly accelerated capabilities of computers, especially artificial intelligence (AI).

The Opportunities

A core challenge with global electrical grids is their growing complexity. Growing exabytes of data from billions of IoT sensors, coupled with too-fast-for-humans reaction speeds required by increasing power demands, make a purely human-controlled electrical grid impossible. To sustain our growing needs, we must resort to a more digitalized approach.

There has been much talk recently about the so-called “smart grid,” defined as an electricity supply network that uses digital communications technology to detect and react to local changes in usage. 16 A smart grid has three major facets: data from sensors, computational power and optimized algorithms. Let us discuss the third point in detail.

Algorithm development has progressed in concert with Moore’s Law – the doubling of the number of transistors on a given semiconductor chip roughly every two years. In the early days of computers, there were a limited number of programming languages – Basic, Fortran, C/C++, Cobol, etc. Nowadays, companies such as Google develop their own proprietary operating systems as a routine course of smart business action. Increasingly, those algorithms are leveraging the power of AI.

While there are many definitions for AI, it can be defined simply as the ability of a machine to perform tasks commonly executed by a human. 17 AI is presently the hottest ICT 18 market sector. PwC estimates that global GDP will increase $15.7 trillion (a +14% boost compared to today) by 2030 as a result of AI. 19 20 Such a huge economic contribution cannot be understated, and organizations are investing aggressively accordingly. Venture capital funding pumped almost $5 billion into AI startups in 2017 alone. 21 AI has become a top corporate spending priority, with many hundreds of billions of dollars devoted to nabbing top talent and to securing algorithmic leadership. 22 There is a global race among governments to capture the title of AI world leader.  23

The reason for this flood of venture capital, corporate and government funding is that AI can solve problems far faster than humans, and in some cases solve problems that no human can. While we humans are smarter than any other living being on Earth, we are still rather dumb when it comes to absorbing and processing quickly vast amounts of data. The human mind is limited also to mostly working on a single task; despite some claims, we are generally horrible at multitasking. 24 Computers don’t have those limitations.

How might AI help the energy sector? We suggest there are two primary means by which advanced AI algorithms can improve efficiency, enhance safety, and improve the bottom line for energy utilities: grid optimization and cybersecurity.

Grid Optimization. To reap the benefits of the smart grid, AI will be a true necessity. “AI will be the brain of this future smart grid. The technology will continuously collect and synthesize overwhelming amounts of data from millions of smart sensors nationwide to make timely decisions on how to best allocate energy resources. Additionally, the advances made from ‘deep learning’ algorithms, a system where machines learn on their own from spotting patterns and anomalies in large data sets, will revolutionize both the demand and supply side of the energy economy.” 25

A challenge for utilities is maintaining consistent power. When there is a sudden increase in demand, the go-to reaction for utilities is to power-up ‘peaker plants’ that run on fossil fuels, generally natural gas. 26 Able to be turned-on within minutes of the detection of a demand spike – for example, due to increased air conditioning or heating requirements from a weather front moving through an area – peaker plants are unfortunately terribly inefficient and polluting. AI might assist in diminishing the use of peaker plants by leveraging advanced forecasting capabilities – for example, considering weather forecasts, regional demand cycles, and smart meter sensors in a single holistic package. The solution of such multidimensional problems is a strength of AI over humans. Such efforts could save utilities significant funds that they could funnel into further grid improvements.

One recent success story in using AI for energy optimization is that of Google’s deployment of algorithms to reduce electricity consumption in their server farms. Google has thousands of data centers worldwide to run search, store and process emails, etc. Those countless banks of GPUs 27 and memory storage systems consume tremendous amounts of electricity and tend to overheat. Because of their increasing presence and inherent inefficiencies, it is estimated that 2% of the world’s energy is consumed by data centers. 28 Both to save costs and to show-off its earlier purchase of the British AI startup DeepMind, Google decided to test AI in improving the cooling efficiency of their computer banks. “The autonomous AI control system initially led to a 12 percent improvement, which over nine months of operation increased to around a 30 percent improvement, with further improvements expected over time as its decisions are improved by having more training data. Google said in the long term that there is potential to apply the technology in other industrial settings.”29

Finally, AI can assist in converting incoming power to be acceptable to a grid. Every energy utility has a mix of power sources that it taps to provide its customer base their electricity. That mix is optimized based on price and, more recently, social perception for use of renewables. A challenge with multiple power sources – natural gas, oil, coal, nuclear, renewables – is to seamlessly integrate them all into the single grid. For example, wind and solar power sources require special converters to enable them to plug into the grid. “The high penetration of renewable energy systems is calling for new more stringent grid requirements. As a consequence, the grid converters should be able to exhibit advanced functions like dynamic control of active and reactive power, operation within a wide range of voltage and frequency, voltage ride-through capability, reactive current injection during faults, and grid services support.”30 AI could assist with energy conversion from renewables by leveraging deep learning to identify key performance optimization criteria and thus enable efficient uptake of the generated power into the smart grid.

Cybersecurity. Ironically, the very action of creating a smart grid makes it more vulnerable to cyber-attacks. Standardizing hardware and software, as well as creating a high degree of connectivity, enables hackers easier access to the grid through a greater variety of means. Programmed backdoors, firmware chip hacks, and even fake chips on motherboards 31 can all compromise the security of a given system.

Humans are inherently limited in what we can accomplish in cybersecurity. Our minds require rest, eating, drinking, etc.; cyber-attack bots require none of that. Thus, even though we might have the best intentions to be ever-diligent in the face of malware or phishing attacks, eventually we will slip up and allow nefarious actors access to sensitive computer systems.

Four years ago, seeing the rapid advances occurring in AI and knowing the challenges of human-centered cybersecurity, one author [TAC] forecasted that soon cybersecurity would enter the realm of “AI vs. AI,” i.e., AI algorithms would be both sources of attacks and defense. We are close to that time now. In the last few years, there has been a surge of interest in coupling the powers of AI, especially deep learning, to cybersecurity. 32 Thousands of companies globally now claim some form of AI in their cybersecurity offerings. Although it is debatable whether all these startups and corporate entities are truly using AI (some startups treat “AI” as a form of pixie dust—sprinkle it in a pitch in attempts to get funding), it makes sense to leverage the powers of learning algorithms to monitor and to react to cyber-attacks.

Ultimately, using AI for cybersecurity may not be just a choice but an imperative in the energy industry. In the United States, the complexity of the grid—with more than 8,000 power plants, 200,000 miles of high-voltage transmission lines, and 5.5 million miles of local distribution lines 33 34—necessitates that even local utilities must think hard about how they monitor their systems for intrusive cyber-attacks.

The Means

So how do utilities leverage the power (no pun intended) of AI? One challenge for cash-strapped utilities is that AI experts are in such demand that a starting salary for a star AI programmer can exceed seven dollar figures35. Thus, a more cost-effective approach may be partnerships among established startups and corporate entities that already have in-house expertise in AI. One such partnership was announced recently between BootstrapLabs (a leading venture capital firm focused on Applied AI in San Francisco) and innogy SE (a leading Germany energy company):

“BootstrapLabs and the innogy Innovation Hub will coordinate globally to build the largest artificial intelligence community for energy ecosystems, and provide a combination of capital and support to Applied AI startups that reimagine the future of energy production, distribution and management across decentralized and interconnected energy services for consumers, machines, enterprises, and public sector agencies.” 36

Sebastian Niestrath, SVP Infrastructure Platform Ventures at innogy New Ventures LLC, further clarified: “The need to interpret massive amounts of data and use AI-supported algorithms for grid operations is becoming increasingly important, especially in Germany – where electricity from renewable sources has pretty much quadrupled during the last 15 years and where millions of solar panels are now installed on residential buildings.” Moreover, with the California Energy Commission unanimously voting 5-0 in favor of mandating that all single-family homes, apartments and condominium complexes of three stories or less require solar panels as of January 2020, California will quickly follow suit in the need for AI-supported algorithms for grid operations.

Further such engagements among all major energy players – including utilities, AI experts, and local/regional/national governments – will be imperative as grid demands and threats increase.

The Future

As our population continues to grow and more technology gets plugged into existing grids, it will be incumbent upon utilities to increase efficiencies to remain cost-effective and to avoid grid failures. Moreover, the growing threat from cyber-attacks will demand that power companies better protect their grids from nefarious actors. AI is one approach that can significantly help utilities move into this challenging world. Opportunities will be strong and risks could be minimized for those leaders who embrace the capabilities of advanced computation.


Authors

Thomas A. Campbell, Special Advisor, BootstrapLabs and Founder of FutureGrasp, LLC

Thomas A. Campbell, Ph.D., is Founder and President of FutureGrasp, LLC (https://www.futuregrasp.com/), which advises organizations worldwide on trends and implications of emerging technologies.

Thomas is also serving as Special Advisor of BootstrapLabs, for Applied AI intellectual propriety development as well as assisting BootstrapLabs to expand its network reach and meet new opportunities into government sectors.

From February 2015 to August 2017, he was the first National Intelligence Officer for Technology (NIO-TECH) with the National Intelligence Council (NIC) in the Office of the Director of National Intelligence (ODNI). Tom’s insights have informed senior policymakers, enabled millions of dollars in industry and academic funding, broken ground in multiple new research areas, and kept diverse groups abreast of the rapid pace and implications of technology change.

Dr. Campbell is focused on emerging & disruptive science and technologies, especially identifying, tracking, forecasting and implications. He has extensive experience in government, academia, and industry – nationally and internationally. More about Tom at this link here.

Nicolai Wadstrom, Founder & CEO, BootstrapLabs

Nicolai is the Founder, CEO and Managing Partner of BootstrapLabs, a leading venture capital firm, based in Silicon Valley and focused on Applied Artificial Intelligence.

Nicolai has spent all of his professional life building technology companies. He started his first business at the age of 15 in the late 1980s and in 2008 he founded BootstrapLabs to build a scalable investment platform focused on being the most valuable partner for entrepreneurs to build successful companies.

With decades of operational and entrepreneurial experience, and having invested in, advised, and mentored over 30 companies, Nicolai works with investment decisions, and post investment mentorship to support scaling for BootstrapLabs’ portfolio companies.

BootstrapLabs is a Venture Capital company focused on Applied Artificial Intelligence that combines Venture Capital and Human Capital to build companies that define our future.

BootstrapLabs unique Venture Builder platform provides all the resources top-tier entrepreneurs need to take their companies from innovation to a Scalable Product Market Fit and from there to growth.

Prior to BootstrapLabs, Nicolai among other things founded a cutting edge Enterprise Software company (in the ECM and EAI space), he also co-founded and IPO’d the first CFD/FSB trading platform in the Nordics, and pioneered Virtual Reality in 1996. More about Nicolai at this link.


References:

  1. “Power is the capacity to use Energy…Power is like the strength of a weightlifter and Energy is the measure of how long he can sustain the output of power…Power is ‘watt’ and Energy is ‘watt-hour’.” http://www.differencebetween.net/science/difference-between-energy-and-power/#ixzz5Sze5vPtN 
  2. C. Klein, (December 17, 2014), “When Edison Turned Night into Day,” History, https://www.history.com/news/when-edison-turned-night-into-day
  3. “World Population by Year,” http://www.worldometers.info/world-population/world-population-by-year/
  4. The population considered in the middle class, and thus more prominent consumers of energy, is exploding globally. H. Kharas, February 17, 2017, “The unprecedented expansion of the global middle class,” Brookings, https://www.brookings.edu/research/the-unprecedented-expansion-of-the-global-middle-class-2/  
  5. “World Energy Outlook 2017,” https://www.iea.org/weo2017/
  6. “Internet of Things (IoT) connected devices installed base worldwide from 2015 to 2025 (in billions),” https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/
  7. Kai-Fu Lee, “AI Super-Powers: China, Silicon Valley, and the New World Order,” 2018, Houghton Mifflin Harcourt, Boston.
  8. Hawkins, April 15, 2018, “World’s third hyperloop test track is now under construction,” The Verge, https://www.theverge.com/2018/4/15/17235262/hyperloop-transportation-technologies-test-track-france
  9. C. Cram, January 7, 2015, “Why Singapore is building a new Indian city 10 times its own size,” The Guardian, https://www.theguardian.com/public-leaders-network/2015/jan/07/singapore-building-india-city-andhra-pradesh
  10. History Editors, August 21, 2018, “2003 Blackout hits Northeast United States,” This Day in History, https://www.history.com/this-day-in-history/blackout-hits-northeast-united-states
  11. The plant where [TAC] worked had diesel generators that kicked-in immediately upon the power loss to avoid thousands of dollars per hour of manufacturing losses. The plant manager nevertheless called FirstEnergy Corporation quickly to find out what happened and when power would be restored. Their unbelievable initial response was, “Don’t worry, we’re on it. We’re watching CNN right now to figure out what happened.”
  12. C. Douris, September 21, 2017, “Utilities Will Spend Billions On Cybersecurity As Threat Grows,” Forbes, https://www.forbes.com/sites/constancedouris/2017/09/21/utilities-will-spend-billions-on-cybersecurity-as-threat-grows/#2dbe47b26cfe
  13. J. St. John, April 17, 2013, “Report: US Smart Grid Cybersecurity Spending to Reach $7.25B by 2020,” GTM, https://www.greentechmedia.com/articles/read/report-u-s-smart-grid-cybersecurity-spending-to-reach-7-25b-by-2020#gs.ZB7iBm8
  14. Department of Energy, Department of Homeland Security
  15. G. Bade, August 7, 2018, “Report: DOE, DHS planning new grid cybersecurity exercise this fall,” Utility Dive, https://www.utilitydive.com/news/report-doe-dhs-planning-new-grid-cybersecurity-exercise-this-fall/529518/
  16. https://www.google.com/ 
  17. Marr, February 14, 2018, “The Key Definitions Of Artificial Intelligence (AI) That Explain Its Importance,” Forbes, https://www.forbes.com/sites/bernardmarr/2018/02/14/the-key-definitions-of-artificial-intelligence-ai-that-explain-its-importance/#15b1f7be4f5d
  18. Information and Communication Technologies
  19. “Sizing the prize – PwC’s Global Artificial Intelligence Study: Exploiting the AI Revolution,” 2017, https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html
  20. PwC, Jue 27, 2017, “AI to drive GDP gains of $15.7 trillion with productivity, personalisation improvements,”  https://press.pwc.com/News-releases/ai-to-drive-gdp-gains-of–15.7-trillion-with-productivity–personalisation-improvements/s/3cc702e4-9cac-4a17-85b9-71769fba82a6
  21. J.D. Rowley, March 2, 2018, “Venture Funding Into AI And Machine Learning Levels Off As Tech Matures,” CrunchBase, https://news.crunchbase.com/news/venture-funding-ai-machine-learning-levels-off-tech-matures/
  22. Seitz, April 6, 2018, “Artificial Intelligence Becoming Top Corporate Spending Priority,” Investors Business Daily, https://www.investors.com/news/technology/artificial-intelligence-ai-spending/
  23. Minevich, December 5, 2017, “These Seven Countries Are In A Race To Rule The World With AI,” Forbes, https://www.forbes.com/sites/forbestechcouncil/2017/12/05/these-seven-countries-are-in-a-race-to-rule-the-world-with-ai/#69c6478b4c24
  24. N.K. Napier, May 12, 2014, “The Myth of Multitasking,” Psychology Today, https://www.psychologytoday.com/us/blog/creativity-without-borders/201405/the-myth-multitasking
  25. Wolfe, August 28, 2017, “How Artificial Intelligence Will Revolutionize the Energy Industry,” Harvard University, http://sitn.hms.harvard.edu/flash/2017/artificial-intelligence-will-revolutionize-energy-industry/
  26. “Peaking Power Plant,” https://en.wikipedia.org/wiki/Peaking_power_plant
  27. Graphics Processing Units, the current workhorse for deep learning AI.
  28. Pearce, April 3, 2018, “Energy Hogs: Can World’s Huge Data Centers Be Made More Efficient?,” Yale Environment 360, https://e360.yale.edu/features/energy-hogs-can-huge-data-centers-be-made-more-efficient
  29. Ranger, August 20, 2018, “ Google just put an AI in charge of keeping its data centers cool,” ZDNet, https://www.zdnet.com/article/google-just-put-an-ai-in-charge-of-keeping-its-data-centers-cool/
  30. Teodorescu, 2011, “Grid Converters for Photovoltaic and Wind Power Systems 1st Edition,” John Wiley & Sons, Ltd., https://www.amazon.com/Grid-Converters-Photovoltaic-Power-Systems/dp/0470057513
  31. Robertson, M. Riley, October 4, 2018, “The Big Hack: How China Used a Tiny Chip to Infiltrate U.S. Companies,” Bloomberg Businessweek, https://www.bloomberg.com/news/features/2018-10-04/the-big-hack-how-china-used-a-tiny-chip-to-infiltrate-america-s-top-companies?srnd=premium&mod=djemceocouncil
  32. “Applied AI Conference 2018 – Panel – Applied AI and Cybersecurity – Making the Enterprise More Secure,” BootstrapLabs, https://www.youtube.com/watch?v=4ckuyHpyA00
  33. December 8, 2017, “How many power plants are there in the United States?,” https://www.eia.gov/tools/faqs/faq.php?id=65&t=2
  34. J. Weeks, April 28, 2010, “U.S. Electrical Grid Undergoes Massive Transition to Connect to Renewables,” Scientific American, https://www.scientificamerican.com/article/what-is-the-smart-grid/
  35. C. Metz, April 19, 2018, “A.I. Researchers Are Making More Than $1 Million, Even at a Nonprofit,” New York Times, https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html
  36. Press Release, October 3, 2018, “innogy Innovation Hub and BootstrapLabs to Form a Partnership for Investments in AI and Energy,” San Francisco Business Times, https://www.bizjournals.com/sanfrancisco/prnewswire/press_releases/California/2018/10/03/NY26733
Nicolai Wadstrom, UnConference, Berlin

BootstrapLabs Joins innogy Innovation Hub at UnConference

Berlin, Germany – October 4, 2018 – Nicolai Wadstrom, BootstrapLabs Founder, CEO and Managing Partner, was recently invited by innogy Innovation Hub to participate in the fourth annual “UnConference” as a panelist to discuss digital disruption, Applied Artificial Intelligence, and the impact of technology on the Energy space.

Nicolai Wadstrom, UnConference, Berlin

During the Disruption Platform session moderated by Thomas Birr, SVP of Innovation & Business Transformation at innogy and CEO of innogy Innovation Hub, Nicolai and Tim Kock, Co-Founder at Jungle AI, weighed in on the challenges and opportunities presented by emerging technologies and their potential impact on the decentralized energy system of the future.

As leaders in the AI space, both Nicolai and Tim were asked to speak about why the unique combination of AI + Energy is so interesting, as well as subtechnologies that have the most potential to disrupt the traditional energy industry.

UnConference by innogy Innovation Hub provides a platform for innovators, startups, VCs, thought leaders, and experts to come together and jointly create the future of energy.

 

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City of Malmö Hosts BootstrapLabs’ Founder Nicolai Wadstrom

Malmö, Sweden – May 8, 2018 – BootstrapLabs Founder and CEO, Nicolai Wadstrom was honored to be invited to speak at Malmö’s legendary Palladium during the city’s 07:07AM quarterly Breakfast with Entrepreneurs.

During Nicolai’s session “AI, Silicon Valley and Malmö! he shared his unique insights in what is happening within Artificial Intelligence, why it is happening now and what lies ahead. He also provided some guidelines on how business leaders can prepare their organizations for the technology shift.

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The business community in Malmö was hungry to learn more about AI and BootstrapLab, the organizers opened up extra tickets and ended up with 500+ registered attendees.

nicolai_wadstrom_malmo_event

07:07 am is a forum where entrepreneurs meet other entrepreneurs and share knowledge and experience. Each meeting has a theme and hand picked speakers for that particular topic. 07:07 am is also an opportunity to meet Malmö city politicians and civil servants.

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Announcing the BootstrapLabs Applied AI Insiders Series: AI and Healthcare

BootstrapLabs Applied AI Insiders HealthTech

BootstrapLabs is thrilled to announce the Applied AI Insiders Series: AI and Healthcare.

Event Summary:

  • Date and Time: Thur, March 8, 2018| 5:30 pm to 8:30 pm
  • Location: Wilson Sonsini Goodrich & Rosati, 1 Market St # 33, San Francisco, California 94105
  • Registration:this event is INVITE ONLY. If you did not receive an invitation you can request one below.

 

 

Event Description

Topic: AI and Healthcare: Impacting the Continuum of Care

We stand in front of the 4th and largest wave of the industrial revolution, powered by Artificial Intelligence and Data. This is the biggest opportunity so far for innovation and entrepreneurship, and every single industry will be disrupted and redefined by companies that are not yet even born.

With the AI economic impact projected to reach $15 Trillion by 2030, we believe Applied Artificial Intelligence represents one of the major wealth creation opportunities of this century.

This BootstrapLabs Applied AI Insiders Series event will focus on how AI is impacting the continuum of care, from prescriptive to predictive, to faster, better, decentralized diagnosis, to pre- and post-operation assistance, and personalized treatment.

CxOs across health organizations, as well as doctors and researchers, are starting to grasp the urgency with which they need to test, adopt, and scale AI technologies across their hospitals, practices, and offices. If the ongoing digital transformation is any indicator, these healthcare providers will struggle to adopt this new wave of artificial intelligence technologies. Today AI is very promising, but many solutions will not become tangible or simply not be resilient enough to survive in the real world.

As a VC focused on Applied AI, BootstrapLabs looks beyond the hype and has assembled an extraordinary panel of speakers to take you through the current state of AI and Healthcare. Read more.

Speakers

Ben Levy, Co-Founder, BootstrapLabs

Ben spent the last 20 years in Silicon Valley building and exiting two FinTech startups to Mergent and NASDAQ, investing in disruptive software technologies, and advising CxOs of Fortune 500 Telecom, Media and Technology companies on corporate strategy, financing and M&A.
Ben is a member of the Association for the Advancement of Artificial Intelligence (AAAI), an Ambassador of La FrenchTech, as well as a frequent keynote speaker at industry conferences on innovation, technology investing, entrepreneurship, artificial intelligence, and globalization in the US, Europe, and Asia.

Wael Salloum, Ph.D., Co-Founder and Chief Science Officer, Mendel.ai

Wael has an MS in Management Information Systems, a BS in Software Engineering, and an AS in Computer Engineering, all from University of Damascus in Syria. At Columbia University, he received an MS and an MPhil in Natural Language Processing, and a Ph.D. in Computer Science (Computational Linguistics).

Paula Wilbourne, Ph.D., Co-Founder & Chief Officer for Mental Health and Wellness, Sibly

Dr. Wilbourne is a psychologist, co-founder and the Chief Officer for Mental Health and Wellness at Sibly, the most effective and affordable mental heal professional in everyone’s pocket, 24/7. She has worked for 20 years conducting research and implementing tools to improve mental health and wellness. She was the Director of Addiction Treatment Services at the flagship VA in Palo Alto.

Thank You To Our Host and Partner

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Applied Artificial Intelligence Conference 2018 #AAI18

Applied-AI-Conference-2018-eventbrite

THE TIME IS NOW: Why you should not wait to invest in AI

The world is buzzing about artificial intelligence, but there are many questions left unanswered, like: How will it affect me? What industries are most vulnerable? Are we ready? While no one can offer a crystal clear vision of all that the future holds, it has been our mission at BootstrapLabs to provide both a point of view and a toolkit for you to make the most of it. Here’s why investing in artificial intelligence is a great move to make right now.

The Data Is Here, Now We Need to Apply It

As with most technological revolutions, the wave of artificial intelligence is breaking on the heels of a promising body of research. Much is possible in theory, but those who take the leap and find working applications of the technology will determine how these innovations play out. That’s how we focus our investments – where can our “cognitive computers” make an impact and generate tangible value?

Over the last decade or so, adaptive and efficiency-driven software has swept up the global economy, reshaping entire industries and redefining the way we do just about everything. Early high-rollers like Google and Amazon created computer systems that leveraged vast amounts of data for consumer benefit and quickly became some of the most profitable and influential organizations in the world. The stage is set for a similar land-grab – and potential takeover – with artificial intelligence. Humans created more data in the last 18 months than in the entire history of the world, and we now have computers which can learn from this data more effectively than humans can. This means there is massive opportunity for investment and innovation, and it’s still early days.

AI Creates a Virtuous Cycle

One beautiful thing about machine learning and generative learning technology is that these systems learn adaptively, rather than following a limited set of rules. For Danny Lange, VP of AI and Machine Learning at Unity Technologies (and formerly Uber, Amazon, Microsoft and IBM), this means that his team can keep “gaining knowledge, then maximizing payout with current knowledge; but keep learning and exploring.” An initial investment in AI would not only help improve the way your business currently operates, but also enable you to innovate more rapidly and prepare for the challenges of tomorrow. With a virtuous cycle in motion between your technology, your processes, and your business strategy, you can exponentially increase the value of your organization.

In every field, such a cycle will enable us to tackle bigger and better problem sets, leading to more engaged and productive employees and, eventually, new job creation. This is true because the advancements that applied artificial intelligence offer have as much, if not more, to do with effectiveness as they do with big software’s efficiency. This is like the Internet squared – augmenting not only our access to information, but also our ability to use the information in meaningful ways. For example, startups like Mendel Health have cropped up for this very purpose. Making better decisions, more consistently isn’t just a good business model for a healthcare company, it’s helping save lives every day.

The Future Is In Your Hands

Of course, AI computers won’t be able to do everything by themselves. Yes, they will be ultra-smart data wizards who can make complex decisions in the blink of an eye. But we’re not talking about sentient robots taking over the world. It is we, the humans, who are building systems to steadily enhance the quality of life on this planet. The technology is enabling us to ask better questions – and give better answers – about what we hope to accomplish in this next wave of industry.

At our second annual Applied AI Conference in May, we were humbled and inspired by the amazing group of leaders across many different disciplines taking this project to heart. Researchers, entrepreneurs, investors, journalists, and more are striving for the most desirable outcomes through open dialogue and belief in a better future. As Hema Raghavan, Head of Machine Learning for Growth at LinkedIn, shared during the Conference, “we can build out the curve where everyone adapts together.” The combined investment of human capital, venture capital, and technology is a large-scale collaboration to build the infrastructure of tomorrow.

To rehash the healthcare example, there are especially huge opportunities for improvement in places where our economics are currently misaligned. Disruptors like visual diagnostic startup Captureproof are already “saving patients 99.996% of their time,” according to Founder and CEO Meghan Conroy, and it’s only a matter of time before a watershed change occurs. From niche applications like Captureproof and Mendel to corporate giants like Amazon and Ford making platform plays, the race is on. If you’re still on the starting block waiting for a cue, let this be your starting gun.

Don’t Wait, Don’t Hesitate

Perhaps the most startling thing about the AI revolution is the pace with which it is moving. Predictions for when a computer would be able to beat a human at Go were shattered by 20 years. In Q1 of 2017 alone, $1.7 billion was spent in seed funding for AI startups. If you’re not thinking about how AI will change your business, someone else is. If you aren’t willing to experiment, you will be left in the dust. The beauty here is that once you get started, there is plenty of room to grow and adapt – that’s exactly what AI does.

We’re right at the cusp of when the behind-the-scenes R&D work is turning into investment, and this investment will soon give way to large-scale market adoption. We’re proud to be at the heart of this exciting industry, and as entrepreneurs ourselves, we are ready to take on the challenges that it brings – together.


NICOLAI WADSTROM

Coding since 10 y/o. Founder, CEO, CTO. Advisor, Parallel Entrepreneur, Operator, Angel Investors.

Nicolai is the Founder of BootstrapLabs, a leading venture capital firm, based in Silicon Valley and focused on Applied Artificial Intelligence. Read more.

Announcing the BootstrapLabs Applied AI Insiders Series: FinTech

BootstrapLabs Applied AI Insiders FinTech

BootstrapLabs is thrilled to announce the BootstrapLabs Applied AI Insiders Series: FinTech.

Event Summary:

  • Date and Time: Mon, December 4, 2017| 5:30 pm to 8:30 pm
  • Location: Google Launchpad Space, 301 Howard Street, San Francisco, California 94105
  • Registration: this event is INVITE ONLY. If you did not receive an invitation you can request one below.

 

 

Event Description

Topic: Fintech: Artificial Intelligence takes on Wall St. and Main St.

The financial sector represents trillions of dollars in annual spending, most of which goes to technology, security, and infrastructure. Yet, the sector is seeing an unprecedented level of VC funding and potential disruption from new (and some already established) startups that are threatening the incumbents on all sides, including retail banking, individual, business, mortgage and auto loans, peer-to-peer payments, portfolio management, savings, bill payments, international wire transfers, and even money itself.

The financial sector is also no stranger to big data, and few industries are as well equipped to benefit from the promises (and suffer from the risk) of Artificial Intelligence.

During our December Applied AI Insiders Series event, we will explore practical ways AI is being applied to deliver new products and services that are faster, cheaper and better, but also share some thoughts on some of the biggest challenges the sector faces with the advances of AI, broader adoption of blockchain technologies, and disintermediation. Read more.

Speakers

Nicolai Wadstrom, Founder, BootstrapLabs

Nicolai Wadstrom, a serial entrepreneur turned parallel entrepreneur as the founder of BootstrapLabs. Nicolai advises all portfolio startups in their day to day operations, connecting founders with industry experts, advisors and investors to increase their likelihood of success, assisting with product design and development, positioning, go-to-market strategy and implementation, partnerships and fundraising.
Nicolai is a frequent speaker, mentor and judge at top Universities and Conferences in the US and Europe on topics such as Entrepreneurship, Innovation, Disruption, Startups and Venture Capital.

Will Summerlin, Founder and CEO, Pinn

Will Summerlin is the Founder and CEO of Pinn, a cybersecurity company with a mission to solve the digital identity crisis. He was previously a founder of Arrive, a software company providing solutions to intelligent transportation providers. He is currently an advisor to Arrive and Story Ventures.

Cathrine Andersen, Co-founder, Roger.ai

After exiting her first company to Cisco in 2014 and helping to jumpstart innovation efforts within AI/ML for this Silicon Valley giant, Cathrine has now co-founded Roger.ai, building the world’s first borderless bill pay and bill lending platform.

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Applied Artificial Intelligence Conference 2018 #AAI18

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