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
BootstrapLabs on Bloomberg News
/in Blog, BootstrapLabs, News, Nicolai Wadstrom /by BootstrapLabsIn 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.
Powering Autonomous Dreams
/in Blog /by BootstrapLabsAuthors
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]
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
BootstrapLabs Applied AI Workshop: AI and Human Capital
/in AI, Blog, Event /by BootstrapLabsDate and Time: Wednesday, January 23, 2019
Location: San Francisco
Registration: This event is INVITE ONLY. You can request more information at events@bootstraplabs.com
Come join us to learn more about how Artificial Intelligence will revolutionize the enterprise of tomorrow, and how Applied AI solutions can help executives and leaders around the world better manage, attract and engage their employees and customers.
As the Fourth Industrial Revolution unfolds, companies are seeking to harness new and emerging technologies to reach higher levels of efficiency and compete on a global scale for innovation, customers, and talent.
Artificial Intelligence is here to stay, and will drastically change the way we do business and live. The economic and social impact of artificial intelligence technologies will ultimately depend on how quickly organizations and institutions are able to learn, adapt, control, and ultimately wield these powerful technologies for the greater good.
Over the last 30 years, the allocation of market capitalization value for corporations has shifted from 80% being assigned to tangible assets (machinery, plants, buildings, etc), to 80% being assigned to their intangible assets (brands, intellectual properties, trade secrets, business processes, and talent).
They are many opportunities and challenges ahead, and as a Venture Capital firm focused on Applied AI technologies, BootstrapLabs believes that the success of any business today, more so than any other time before, will ultimately be determinate by their ability to not only adopt AI solutions across their entire business but also, and maybe most critically, apply AI to known critical factors such as attracting, retaining, developing and managing their pool of Human Capital as well as delighting their customers.
Agenda:
Speakers:
Ben Levy, Co-Founder & Managing Partner, BootstrapLabs
Jonathan Eisenzopf, CEO, Discourse.ai
Natalie A. Pierce, Partner, Littler
Sabrina Atienza, Co-Founder & CEO, Stealth Mode Startup
Debi Hirshlag, Advisor and former VP, HR at Workday, Flex, Trimble & Ariba
Thank You To Our Host
b.telligent and BootstrapLabs partner to accelerate the adoption of AI technology among German and Swiss Corporations
/in AI, artificial intelligence /by BootstrapLabsSAN FRANCISCO, CALIFORNIA, US and MUNICH, GERMANY, Dec. 20th, 2018 /PRNewswire/ – b.telligent, a Munich and Zurich based premier data science and business intelligence management consulting company, and BootstrapLabs, a Silicon Valley based venture capital firm focused on Applied Artificial Intelligence, today announced the formation of a strategic partnership to accelerate the deployment of AI technology among b.telligent’s existing and future corporate customers.
Accenture estimates that AI has the potential to boost rates of profitability by an average of 38% and could lead to an economic boost of US$14 trillion by 2035 globally. With so much at stake, and a possible reallocation of wealth from traditional players to digital first players, Germany and Switzerland are among the countries with the most to lose if their multinational corporations fall behind during the AI revolution.
“We are excited to partner with b.telligent,” said Nicolai Wadstrom, Founder, CEO, and Managing Partner of BootstrapLabs. “Klaus and Sebastian have assembled a great pool of talent with deep domain knowledge expertise in data warehousing, data management, data science, and data strategy that helps their clients successfully transition into the new data driven world. As such, they represent an ideal partner to roll out some of our portfolio companies’ products across their client base of over 300 companies, and in doing so, accelerate the adoption of innovative AI solutions for Germany and Switzerland, and some of their leading multinational corporations.”
For the past 3 years, BootstrapLabs has been exclusively investing in Applied AI technologies and has assembled an exciting portfolio of 22 companies, each looking to solve hard and meaningful problems in multibillion dollar industries.
“Startups often waste time and capital figuring out which of the several large corporations they are talking to today i) can quickly and fairly evaluate their solution, ii) has the budget to purchase their solution in the next 3-6 months, and iii) is ready to scale that solution across their entire organization,” said Benjamin Levy, Co-Founder and Managing Partner of BootstrapLabs.
“b.telligent is in an incredible position to accelerate BootstrapLabs’ portfolio companies’ revenue generation, as we not only have our customers’ trust (such as Telefónica, Car2Go, Puma or BSH), but we have an intimate understanding of their readiness level as well as often act as an extension of their team, managing the very data that is required for these AI solutions to become valuable to our clients,” said Sebastian Amtage, Managing Director of b.telligent.
“We believe that Artificial Intelligence poses a large threat, as well as unprecedented opportunity for every company, and we look forward to working closely with BootstrapLabs, their portfolio companies, and their large community of AI experts to support our customers on their journey to apply Artificial Intelligence,” said Klaus Blaschek, Co-Founder and Managing Director of b.telligent.
Media Contact
BootstrapLabs
press@bootstraplabs.com
b.telligent GmbH & Co. KG
pr@btelligent.com
Greta Wenske
About BootstrapLabs
Founded in 2008, BootstrapLabs is a leading Venture Capital firm based in Silicon Valley and focused on Applied Artificial Intelligence. We invest in founders that dream big and are solving today’s hardest problems by applying artificial intelligence to shape a better future for all.
BootstrapLabs works closely with some the world’s most prominent families and their multinational corporations to actively invest in artificial intelligence (AI) and machine learning (ML) startups targeting large global markets including Industrial Manufacturing, Transportation, Logistics, FinTech, Enterprise Productivity, Cybersecurity, and Healthcare.
BootstrapLabs is often the first institutional money and acts as a lead investor in the early stages, with follow-on capital at the growth stages.
Select portfolio companies include AEye, Mendel, Qurious, Vidora, Sibly, Myia Labs, Indus.ai, iUNU, SmartEar, Nodle.io, Roger, Prezi, Pryon, Trusted Insight, and AngelList.
BootstrapLabs leverages its large community of over 40,000 Applied AI experts, entrepreneurs, and developers to support its portfolio companies and investment strategies.
For more information visit: https://bootstraplabs.com
About b.telligent
b.telligent is considered one of the market leaders in Germany for business intelligence consulting projects. The main objective of b.telligent is to enable clients to tackle the challenges of the present and upcoming digital transformation. In order to meet those challenges, it is important for b.telligent to support its clients in the deployment of some of the most advanced data science techniques such as artificial intelligence and machine learning while at the same time ensuring these ventures are based on solid data fundamentals.
Additionally, b.telligent continuously strives to optimize clients business processes by applying knowledge gained through consolidation and analysis of business information all resulting in increased margins, lowered cost and improved risk management.
Our customers are leaders in their sectors, such as telecommunications, financial services, trade, and the industry. Since 2004, over 300 clients have benefited from our “data-first” approach. With over 160 employees spread across six offices, we are passionate about joining our clients in establishing and advancing their data-driven business models.
For more information visit: https://btelligent.com
BootstrapLabs AAI18 Keynote and Fireside Chat – Death of Moore’s Law
/0 Comments/in AI, artificial intelligence, Blog, BootstrapLabs /by BootstrapLabsOver the next several weeks we’ll be releasing the videos from the sessions from the BootstrapLabs Applied AI Conference 2018. The yearly conference organized by BootstrapLabs, a leading Venture Capital firm focused on Applied AI, that brings together over 800 members of the Artificial Intelligence community for a day of incredible speakers and exciting conversations.
Speakers:
During this session, the speakers discussed the future of computing in the age of AI, Neuromorphic and Quantum computing and edge use cases and applications.
Some of the key takeaways from the session are:
About Tom Campbell:
Thomas A. Campbell, Ph.D.is the founder and president of FutureGrasp, which advises organizations on trends and impact of emerging technologies, and a special advisor to BootstrapLabs. Prior to this, Tom was the first National Intelligence Officer for Technology with the National Intelligence Council. Tom holds a Ph.D. in Aerospace Engineering Sciences from the University of Colorado at Boulder, and a B.E. in Mechanical Engineering from Vanderbilt University.
About Madhav Thattai:
Madhav Thattai is the COO of Rigetti Computing, a full-stack quantum computing company based in Berkeley, California. Prior to joining Rigetti in 2015, Madhav worked at Dell Computing for eight years in a variety of roles, most recently as Director of Product Operations. Madhav holds a Master’s degree in management from Stanford University, a Master’s degree in industrial engineering from the University of Michigan, and a Bachelor’s degree in mechanical engineering from Birla Institute of Technology and Science in India.
About Sateesh Kumar:
Sateesh is the Founder and CEO of Pathtronic Inc, a silicon valley start-up company focused on building revolutionary AI Processing Unit. Sateesh is a visionary and seasoned entrepreneurial executive and innovator with broad technology background. He has been advisor to multiple corporations, start-ups, incubators, and universities in the area of AI, Autonomous Vehicles and Drones, and more. As a former innovation and incubation executive at Cisco, he pioneered and led various initiatives in the area of IOT, Automotive and Connected Vehicles, Industrial Switching and Automation, Smart Cities, Mobile and Wireless, Fog Computing, Big Data and Machine Learning and more. Prior to Cisco, he founded and led multiple Silicon Valley venture funded start-ups. He has published extensively in international journals including IEEE/ACM and holds dozens of granted patents.
Banish the Darkness with Artificial Intelligence
/in AI, AI News, artificial intelligence, Blog, Energy, News, Silicon Valley /by BootstrapLabsThe 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 3 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.7 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.
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The Best AI Conferences in 2019
/in AI, AI News, artificial intelligence, Blog, Event, News, Silicon Valley /by luigicongedoBootstrapLabs is pleased to announce that our annual Applied AI Conference has been selected by Hewlett Packard Enterprise, in the article “The 16 AI and ML conferences you should attend in 2019” as one of the top AI events to attend next year.
This recognition is the latest in a series of recommendations we have received from many AI experts, companies, and journalists, including Business Insider last year.
VC investment in artificial intelligence and machine learning doubled from $6 billion in 2016 to $12 billion in 2017 (Source. KPMG), and investments are at an all time high in the space.
With the AI market projected to grow over 20 fold in the next 10 years to $3 trillion annually, we believe Applied Artificial Intelligence represents one of the major wealth creation opportunities of this century.
If you want to increase your knowledge and connections in the industry, become an AI champion inside your organization, or find new career opportunities, the Applied AI Conference 2019 would definitely be a great event to attend. Discount tickets and more information are available on our conference web page – link here.
BootstrapLabs also has a limited number of sponsorship opportunities available for select partners; you can learn more about these opportunities and request more information at this link.
Below you can find the article from Hewlett Packard Enterprise:
The 16 AI and ML conferences you should attend in 2019
If you want to grow your career, consider attending one of these great artificial intelligence and machine learning conferences for IT pros. Whether you’re a manager, developer, or researcher, doing so will help you meet expert colleagues and improve your skills.
AI is as hot as a laptop with a broken fan—so scorching that some conferences promise to exclude recruiters. As such, there are plenty of organizations motivated to share AI and machine learning information. This overview aims to help you identify the conferences that are worth your time and meet your needs.
At first glance, you could use a background in data mining just to sort through all the events that have “artificial Intelligence” in their titles or include AI conference tracks. I winnowed down the offerings based on the quality of speakers, attendees, and networking opportunities. However, your own needs are the ultimate filter: Are you a programmer looking to increase your skills? An executive looking for competitive insights? A researcher looking to share ideas? A tech recruiter looking for prospects? A prospect looking to dodge recruiters? Some conferences are large and multifaceted enough that nearly anyone can get something out of them. Others appeal only to researchers in subspecialties.
The list below aims to give you enough details to make an informed decision. It is ordered by date, with gold stars for the conferences I think merit close attention. The conference list starts in late 2018, in part because few organizations have published details about fall 2019 events; the coming-soon events provide enough information for you to decide whether they should be on your schedule for next year.
Full article available at – https://www.hpe.com/us/en/insights/articles/the-16-ai-and-ml-conferences-you-should-attend-in-2019-1811.html
BootstrapLabs is pleased to announce our next Applied AI Insiders Series event: Artificial Intelligence & Energy
/in AI, AI News, Blog, Event /by luigicongedoArtificial Intelligence & Energy, in partnership with the innogy Innovation Hub.
Date and Time: Wednesday, December 12, 2018 | 5:30 pm to 8:30 pm
Location: Galvanize, 44 Tehama Street, San Francisco, California 94105
Registration: This event is INVITE ONLY. If you did not receive an invitation you can request one below.
We stand in front of the 4th and largest wave of the industrial revolution, powered by AI 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 market projected to grow over 20 fold in the next 10 years to $3 Trillion annually, we believe Applied Artificial Intelligence represents one of the major wealth creation opportunities of this century.
Our energy system is changing rapidly – becoming more digital, decentralized and more democratic. For example, we are witnessing the rise of “prosumers” who are taking energy generation and management into their own hands. So what does the future energy systems look like?
We believe this future cannot be built by iterating on the status quo, but by carrying out radical innovation – including harnessing the ever-increasing amount of data and applying new technologies like AI. Artificial Intelligence will transform the way we produce, distribute, and consume energy – continuously collecting and analyzing data from millions of smart sensors, and making smart decisions on how, where, and when to allocate energy more efficiently.
Come join us to learn more about how Artificial Intelligence will revolutionize the energy industry.
BootstrapLabs and the innogy Innovation Hub are working globally to build the largest artificial intelligence community for energy ecosystems, and provide a combination of capital and support to Applied AI startups.
This is an INVITATION ONLY event with very limited capacity. BootstrapLabs reserves the right to cancel any tickets.
Contact events@bootstraplabs.com to request additional invitations.
AGENDA:
5:30 pm – 5:50 pm | Registration & Networking
5:50 pm – 6:10 pm | Welcome Message and Introduction by BootstrapLabs
6:10 pm – 7:40 pm | AI and Energy Keynotes
7:40 pm – 8:30 pm | Networking and drinks
SPEAKERS:
Ben Levy, Co-Founder, BootstrapLabs
Ben has been actively investing in technology startups and supporting BootstrapLabs’ portfolio companies. Born in France and living in Silicon Valley for the past 20 years, Ben is a repeat entrepreneur who launched, built and exited two startups in the financial technology space.
Earlier in his career, Ben was an Investment Banker who advised CxOs of Fortune 500 companies and startup founders on corporate strategy, financing, and M&A. His banking experience spans across firms like Lazard, SG Cowen, Houlihan Lokey, Wedbush Securities, and QuantumWave Capital.
Ben is also a member of AAAI (Association for the Advancement of AI), and a frequent keynote speaker on innovation, technology investing, entrepreneurship, and artificial intelligence in the US, Europe, and Asia.
Thomas Birr, SVP Innovation & Business Transformation, innogy SE and CEO, innogy Innovation Hub
Thomas is SVP Innovation & Business Transformation at innogy SE and CEO of the innogy Innovation Hub. Innogy is a leading European utility which mission is to create a future beyond the traditional energy business. The innogy Innovation Hub is building and co-creating new digital and platform-powered business models in partnership with the world’s most promising start-ups. Thomas is a strong believer that AI will change every industry – including Energy.
Sebastian Niestrath, SVP Infrastructure Platform Ventures at innogy New Ventures LLC
Sebastian Niestrath is a venture capitalist with innogy New Ventures in Palo Alto, California – part of innogy, one of Europe’s leading electric utilities. During his 12 years in the energy industry, Sebastian has gained extensive experience across the entire energy value chain, his area of expertise is energy markets and energy trading. Sebastian is passionate about working closely with startups that build the digital and decarbonized energy system of the future.
Michael Wara, Director, Climate and Energy Policy Program, Woods Institute for the Environment, Stanford University
Michael Wara is Director of the Climate and Energy Policy Program at the Woods Institute for the Environment and research fellow at the Steyer-Taylor Center for Energy Policy and Finance at Stanford.
Wara’s legal and policy scholarship focuses on carbon pricing, energy innovation, and regulated industries. He collaborates with economists, engineers, and scientists in research on the design and evaluation of technical and regulatory solutions to climate and energy challenges. He is also an expert on international environmental law with a particular focus on the ozone and climate treaty regimes.
Wara received his J.D. from Stanford Law School and his Ph.D. in Ocean Sciences from the University of California at Santa Cruz.
Prateek Chakravarty, Head of Worldwide Sales, Bidgely
Prateek leads worldwide sales efforts at Bidgely, a software company bringing AI solutions to the modern utility and energy retailer.
Before Bidgely, Prateek worked in leadership roles at Pacific Gas & Electric Company and Schlumberger. He holds a Bachelor of Technology degree from the Indian Institute of Technology and an MBA from Harvard Business School.
The innogy Innovation Hub is the Accelerator and Venture Capital arm of innogy SE, Germany’s leading energy company.
innogy SE is Germany’s leading energy company, with revenue of around €44 billion (2016), more than 40,000 employees and activities in 16 countries across Europe. With its three business segments Grid & Infrastructure, Retail and Renewables, innogy addresses the requirements of a modern, decarbonized, decentralized and digital energy world.
Founded in 2008, BootstrapLabs is a leading Venture Capital firm based in Silicon Valley and focused on Applied Artificial Intelligence. We invest in founders that dream big and are solving today’s hardest problem by applying artificial intelligence to shape a better future.
We are pleased to announce that the annual BootstrapLabs Applied Artificial Intelligence Conference will return for the fourth year on April 18, 2019 in downtown San Francisco.
The BootstrapLabs Applied AI Conference 2019 is a must-attend event for those involved or interested in the most current AI technologies and products.
The one day conference brings together the brightest and most experienced professionals in the field of AI for an immersive day of learning, discussion, and connection. This year’s agenda will focus on the latest trends and the future impact of AI applications and commercialization across a breadth of sectors, including Transportation, Logistics, Health, Energy, FinTech, the Future of Work (FoW), Internet of Things (IoT), and Cybersecurity.
A limited number of tickets are currently on sale at the Super Early Bird price ($1200 off)! Join thought leaders leveraging AI applications to build the future of enterprise, corporations, governments, and society as we know it. Register and learn more at this link.
Applied AI Digest 134
/in Applied AI Digest /by BootstrapLabsThe latest Data, Insights, and Inspiration about Applied AI
Every week the BootstrapLabs team curates the most interesting and current articles on Artificial Intelligence. Here are our picks for the third week of October 2018.
Applying Artificial Intelligence in B2B and B2C – What’s the Difference?
We discussed the difficulties large businesses may have in adopting AI in our previous article; despite this, last month we fleshed out the reasons why… read more
How Autonomous Delivery Drones Could Revolutionise Military Logistics
The UK’s Defense Science and Technology Laboratory (Dstl) is working with other government agencies to develop drones and unmanned ground robots with autonomous ‘deliver to order’ capability for frontline military logistics support. Julian Turner analyses the tech of the future… read more
MICROSOFT’S NADELLA SAYS AI CAN MAKE THE WORLD MORE INCLUSIVE
TALK OF ARTIFICIAL intelligence often leads to speculation about how machines may displace workers. Microsoft CEO Satya Nadella thinks we should talk more about how AI algorithms can expand the workforce now… read more
Artificial intelligence is complex, but we can’t afford to ignore it
In early September, the media went abuzz with news of Amazon becoming the second US company to surpass the $1 trillion market cap. However, the news of Amazon’s success has eclipsed certain interesting facts… read more
M.I.T. Plans College for Artificial Intelligence, Backed by $1 Billion
Every major university is wrestling with how to adapt to the technology wave of artificial intelligence — how to prepare students not only to harness the powerful tools of A.I… read more
Upcoming Events
The BootstrapLabs Applied Artificial Intelligence Conference represents a unique opportunity to deepen your knowledge about trending AI applications and technologies. Each year, we invite world class perspectives from research, entrepreneurship, investing, and business transformation.
A limited number of tickets are currently on sale at the Super Early Bird price ($1200 off)!
Save the Date: April 18, 2019
Leaders in AI
In case you missed our BootstrapLabs Applied Artificial Intelligence Conference 2018, we’d like to share with you a portion of the highlights and discussions about interesting sectors like Health, Transportation, Logistics, Energy, and more.
Check out our 2018 Conference Highlight Video below along with a panel discussion on “AI Policymakers: The Need for Public/Private Partnership.” If you’re curious and would like to explore more topics on AI, see our YouTube Channel.
AAI18 Conference Highlights
AI Policymakers: The Need for Public/Private Partnership
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BootstrapLabs AAI18 – Panel Discussion – AI Policymakers: The Need for Public/Private Partnership
/in AI, AI News, Blog /byThe topic of this panel focuses on the challenges of AI deployments and their inherent limitations. Panelists also highlight how to help policymakers learn about the complex ecosystem of technology so that they are able to make informed decisions for the public good.
Moderator:
Jane Macfarlane, CEO, Seurat Labs / Director – Smart Cities, UC Berkeley
Speakers:
Kay Firth-Butterfield, Head of AI and Machine Learning, World Economic Forum
Norma Krayem, Sr. Policy Advisor and Co-Chair – Cybersecurity and Privacy, Holland & Knight
Raj Minhas, VP – Interaction and Analytics Lab, PARC
During this session, the panelists discussed AI in the context of regulation, the role of government in fostering and leveraging emerging technologies and the reasons why we need better public/private collaboration.
Some of the key takeaways from the session are:
About BootstrapLabs Applied Artificial Intelligence Conference:
The BootstrapLabs Applied Artificial Intelligence Conference is a yearly conference organized by BootstrapLabs, a leading Venture Capital firm focused on Applied AI. The annual event brings together over 800 members of the Artificial Intelligence community for a day of incredible speakers and exciting conversations. The community is comprised of founders and executives who are experts in the field of Applied Artificial Intelligence, veteran technologists leading the world’s most innovative organizations, and key partners in the AI ecosystem sharing deep-domain knowledge within specific verticals.
If you would like to attend the BootstrapLabs Applied Artificial Intelligence Conference 2019, get your tickets now!
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