The Tech Tribune staff has compiled the very best tech startups in Glendale, California. In doing their research, they considered several factors including but not limited to:
1. Revenue Potential
2. Leadership Team
3. Brand/Product Traction
4. Competitive Landscape
Additionally, all companies must be independent (un-acquired), privately owned, at most 10 years old, and have received at least one round of funding in order to qualify.
Click here to see the full list.
Beyond Limits, a leading developer of advanced artificial intelligence (AI) solutions, today announced its participation at the Wonder Women Tech National Conference 2018. The event will take place from October 5–6 at the Long Beach Convention Center. The conference will be hosted by Wonder Women Tech (WWT) to promote underrepresented entrepreneurs and innovators in science, technology, engineering, arts, and math (STEAM). The two-day conference will feature keynote speeches and discussion panels on some of today’s hottest tech topics and address the unique challenges that minorities of all ages, ethnicities, and genders face in the tech industry.
Beyond Limits’ outreach goals are closely aligned with those of WWT, a nonprofit organization that supports, promotes, and encourages underrepresented people in technology industries via national and international conferences and programming. As a company that recruits and benefits from talented people in the STEAM community, Beyond Limits is proud to collaborate with WWT to raise awareness for workplace diversity and inclusion. The company’s commitment to these issues is based on the collective welfare of its employees and awareness that diversity and inclusion are competitive advantages.
According to the World Economic Forum, 60 percent of college graduates are women, but account for only 35 percent of the total number of undergraduate degrees in science, technology, engineering, and math (STEM). 40 percent earn degrees in mathematics, while a mere 18 percent earn degrees in engineering or computer science. A 2010 research report conducted by the American Association of University Women (AAUW) concludes that this disparity is the result of young women’s social environment, specifically at home and at college, and well-documented issues with gender bias and sexual harassment.
“Diversity impacts everyone,” said Mario Portugal, Beyond Limits head of recruiting. “We welcome the opportunity to support talented people of all genders, cultures and backgrounds with STEAM skills, and we’ll continue to do so in the future.”
In addition to Wonder Women in Tech, Beyond Limits’ commitment to diversity has included recruiting and networking events hosted by WomenHack LA and Reed Smith’s Diversity Summit. These events are designed to create awareness about the importance of diversity and connect top talent in product management, software development, and UI/UX design to tech companies that value inclusion and diversity.
Launched in 2014, Beyond Limits produces cognitive AI systems with human-like reasoning available to transform the performance of industrial and enterprise operations and systems. The company leverages advanced technologies developed at Caltech’s famed Jet Propulsion Laboratory (JPL) for the NASA space program, as well as breakthrough technology innovations originated by Beyond Limits scientists and engineers.
Beyond Limits is the only AI company in the world with advanced technology proven in extreme environments from space missions to subsurface oil and gas exploration. Today, Beyond Limits goes beyond conventional AI, applying cognitive AI software inventions to solve complex industrial and enterprise problems, bringing cognitive advantages to energy, fintech, healthcare, and logistics.
Beyond Limits was selected as a Silver Stevie® Award Winner in the 2018 American Business Awards®, a winner for the Tech Trailblazers Award in the category of artificial intelligence, and has been covered in the Wall Street Journal, Financial Times, Forbes, Fortune, Entrepreneur, ZDNet and the International Business Times.
Read the full Forbes article here.
For scale, consider the Statue of Liberty, standing 305 feet tall. At 466 feet, the average wind turbine in the U.S. dwarfs Lady Liberty by more than half. And when GE’s next-generation monster wind turbine, the Haliade-X, hits the market in 2021, it will nearly double that size to 877 feet, just shy of the Eiffel Tower. A single Haliade-X rotor blade will stretch 315 feet, longer than a football field.
As a general rule of thumb, when it comes to energy and energy exploration, bigger is better: the larger the machinery, the deeper the dig, the greater the production yield. But this massive scale can pose major challenges. By necessity, assets like oil rigs, wind farms and mines are often located in remote and harsh environments, posing safety risks to human workers during construction, inspections and repairs. Equipment laden with sensors can collect petabytes of data, but without reliable high-speed wireless infrastructure, transmitting it can be slow and unwieldy, straining the system’s bandwidth.
That’s all changing—and fast, thanks to rapidly evolving and emerging 5G, AI and IoT technology. Collectively, they’re transforming the energy sector in fundamental ways, by enabling energy and mineral harvesting optimization, predictive and automated maintenance, high-volume and low-latency data delivery, and smarter power grid management for better allocation of energy resources to countries, cities, manufacturers and consumers.
“Together these technologies are improving efficiency, driving down costs and allowing companies in those spaces to make better use of the available assets,” says Paul Miller, senior analyst at Forrester Research. “The biggest impact is around asset visibility and asset management. What is my equipment doing? Is that wind turbine turning and how much is it producing?”
But the benefits of today’s tech advances go beyond ensuring that machinery will perform at peak capacity. Miller sees IoT and AI’s impact transforming the very concept and function of a city’s power grid, which has remained structurally unchanged since the 1930s.
“The energy grid for a city is a mix of different sources: nuclear, gas, wind and solar,” he says. “You’re going to want to use renewable energy as much as possible, but you have to make a guess for how much energy is going to be needed by the city. Essentially you want as much data as possible to make those guesses as data-driven as possible.”
“The ultimate impact will be for every city and every country optimizing their use of power so that you need to produce less,” Miller says.
Viewed up close, current tech advances in the energy and mining sectors look like a patchwork quilt of isolated improvements, but the big picture shows something more sweeping. Here are four key arenas where AI and IoT are changing the game in the energy and energy exploration sectors.
By 2020, the industrial IoT is expected to comprise more than a trillion sensors, each collecting and sharing data in real time. This mountain of data, when processed and analyzed by advanced machine learning software, will let energy companies monitor and regulate production to cut costs and maximize output—down to the minute.
A McKinsey study projects that AI innovations could save oil and gas companies as much as $50 billion in production costs annually. Among the companies innovating in the synchronization of AI, IoT and oil and gas hardware is Calgary-based Ambyint, whose intelligent “adaptive controller” platform samples data from thousands of vertical and horizontal oils wells every five milliseconds to recommend optimization strategies. San Francisco–based Tachyus also integrates real-time equipment data with seismic activityto regulate maximum oil flow through pipelines.
Predictive AI is even helping improve how oil and gas companies locate the most resource-rich drilling grounds. Chevron is using AI to identify new well locations in California; by drilling in better locations, production has risen by 30%, the oil giant claims. Recently, BP invested $20 million in Beyond Limits, an AI startup commercializing cutting-edge tools from NASA to adapt deep-space exploration technology to deep-sea oil and gas exploration in the search for promising drilling grounds.
For the wind and solar power industries, AI is enabling greater energy yield through advanced weather forecasting and analysis. How do you maximize wind power when the wind dies down, or solar power during overcast days? By incorporating intelligent “tuning” mechanisms into the hardware that automatically adjust control settings for varied weather conditions.
GE Renewable Energy is taking a different tack to optimize wind power by creating digital wind farms. These “digital twins” are virtual models of actual wind farms that gather data from the physical turbines during operations and analyze potential settings to determine optimal efficiency. GE reports that its digital-twin technology will boost energy production by 20% annually, generating $100 million more profit over the lifespan of a typical 100-megawatt wind farm.
Predictive Maintenance And Cognitive Vision
In northeast Iowa, on a blustery day in March recently, a wind turbine’s blades churned steadily. But 400 miles away, data analytics software detected an anomaly: Unexpectedly, the turbine’s gearbox was on the verge of failure. The wind farm’s operators quickly dispatched a crew for a $5,000 repair job, averting a catastrophic breakdown costing several days of downtime and $250,000 in lost revenue.
But predictive maintenance, enabled by AI and IoT, isn’t just about preventing unforeseen equipment failure. By predicting wear and tear, it allows timely maintenance that can extend the life cycle of complex and costly machinery. More importantly, it can ensure the safety of human crews who scale massive equipment while exposed to the elements or who must attempt a dangerous rescue mission after a mine collapse.
Much of predictive maintenance technology today is enabled by sophisticated IoT sensors inside machinery to monitor temperature, moisture, output flow and seismic vibrations. Externally, AI-enhanced drones and robots are proving equally valuable in revolutionizing inspections and repairs.
Among the powerful new tools for monitoring outdoor machinery such as oil rigging and wind turbines is Aerialtronic’s “digital vision” platform, a camera-computer hybrid that can be mounted to drones or mobile robots. Its optical and thermal cameras, along with an onboard 1.5-teraflop GPU, let it detect even the tiniest of fissures that could lead to equipment failure. Another digital vision system from SkySpec lets an autonomous flying drone inspect an offshore wind turbine in less than 15 minutes. If it finds damage, its analytics can project repair costs and calculate whether they’re worthwhile, or if it’s more cost-effective to replace the equipment.
Environmental And Safety Upgrades
A recent survey asked executives from 100 of the largest mineral extraction companies in the world to name their top priority for deploying IoT in mining operations and 47% of them gave the same answer: monitoring their mines’ environmental impact. The reason? Meeting strict government regulations on environmental impact is costly, but an even greater responsibility is ensuring the health and safety of miners.
Companies like Inmarsat are working with mining companies to leverage IoT and machine learning to bolster worker safety and environmental compliance using smart sensors. Wireless sensor networks provide early detection of excessive vibrations that could lead to structural collapses, as well as the presence of dangerous flammable and combustible gases such as methane and carbon dioxide. Data collected by these sensors, as well as workers’ wearable sensors and sensor-laden flying drones used to conduct site surveillance, helps mining firms generate predictive models to minimize future dangers. All told, experts predict that smart sensors could save the mining industry $34 billion in costs by reducing health and safety incidents.
The nuclear power industry is also tapping machine learning to improve reactor safety, which could strengthen it as an alternative power source in the U.S.; currently, nuclear power plants provide 20% of all electricity generated. (Nuclear power safety is no small concern. Since the 1986 Chernobyl disaster, 56 of 99 major nuclear power accidents have occurred in the U.S.)
Engineers at Purdue University have created a deep learning neural network that can detect minute cracks within nuclear reactors by rapidly analyzing video images, which until now has been a lengthy, tedious and imprecise job for human inspectors. Rendering the inspection task even more difficult? Large sections of nuclear reactors are underwater and difficult to monitor.
Trained on a dataset of 300,000 images of crack and non-crack examples, Purdue’s AI has scored a 98.3% success rate in identifying tiny fissures in reactor walls—a significantly higher rate than that of human inspectors.
Autonomous Energy Production
In a report on the future of AI for the renewable energy industry, global risk management consultants DNV GL envision a day when wind and solar farms could spring up without any human involvement. Self-driving trucks could transport wind turbine and solar array components from the factory to the site. Another set of robots would unload and assemble them on a foundation dug in earth and filled with concrete by more robots. Finally, drones and robots would assemble the working facility.
Far-fetched? Not entirely. Autonomous mining is already underway in Boliden, Sweden’s Kankberg gold mine, and plans include its eventual operation without any human workers. In conjunction with the Swedish government, the mine’s operator has teamed up with telecom giant Ericsson, Volvo and Abb on the innovative project.
Self-driving excavators and haulers remove minerals from the 500-meter deep site. A 5G wireless network connects all machinery and sensors to ensure seamless production, transmitting data at 100 gigabits per second, nearly 100 times faster than current Wi-Fi technology.
No human workers mean no humans are at risk from mining accidents or disaster. A 24/7 production cycle optimizes value for mining companies. All told, the benefits of autonomous energy production are clear. Even if its arrival as a reality is far off, one by one the pieces are falling into place thanks to the convergence of 5G, AI and IoT. Together these advances are disrupting the energy sector at every stage, from production to refinement and consumption. In the coming decade, you can expect to see this sweeping digital transformation pay off in lower-cost, lower-risk and higher-yield businesses.
Read the full Forbes article here.
Cognitive AI in edge devices will enable the Internet of Things (IoT) to become more than a collection of sensors. AI on a Chip promises a revolution in IoT applications because it delivers actionable intelligence at the edge, where it previously was impossible.
We live in a digital world where everything can potentially be connected to everything and generate data. Data is a core resource and we need to capitalize on it. Instead of simply sensing our environment, we can transform it into something that is safer, more profitable, and insightful. The key is actionable intelligence, which is data and information that can be immediately acted upon without further processing by man or machine.
In its varied forms, from the mysterious brain of the octopus and the swarm intelligence of ants to Go-playing deep learning machines and driverless vehicles, intelligence is the most powerful and precious resource in existence. Despite recent advances in Artificial Intelligence (AI) that enable it to win games and drive cars, there are countless untapped opportunities for intelligence to have a significant impact on making the world a better place.
Cognitive AI is not about chatbots, talking virtual assistants, or playing chess against a machine. It’s about powering the next generation of commercial and industrial IoT edge devices, making it possible to apply them in scenarios that we can only dream about right now.
Businesses are eagerly embracing the Internet of Things and its potential to make new product offerings possible, provide actionable business insights, lower costs, and increase productivity and safety. BI Intelligence predicts that 34 billion devices will be connected to the internet by 2020, up from only 10 billion in 2015, with businesses being the top adopters of IoT solutions. A recent study by International Data Corporation (IDC) projected worldwide spending on the IoT to reach $772.5 billion in 2018, up from $674 billion in 2017, and surpassing $1 trillion in 2020.
Leading the pack in IoT spending this year will be the manufacturing ($189 billion), transportation ($85 billion), and utilities ($73 billion) industries. Healthcare is also a bright spot for the Internet of Things. The global market for wearable connected medical devices is projected to reach nearly $19.5 billion in 2021, up from $5.5 billion in 2016.
Connectivity, Intelligent Analysis Abilities Remain Stumbling Blocks
As the number of commercial and industrial IoT devices proliferate, connecting them and getting them to behave intelligently are among the biggest challenges to IoT realizing its full potential. An industrial facility might have 20-30,000 sensors monitoring status of thousands of machines and processes. But they often reside in silos that do not communicate. Some AI solutions are dependent on a cloud service architecture. Essentially a mainframe approach with centralized computing.
But in many industrial locations, sufficient bandwidth or even connectivity cannot be relied upon. Sensor data needs to be collected, correlated with historical performance data, and analyzed to provide actionable information and make decisions in real-time. There’s no time to reach out to the mother ship for answers. One important strategy for obtaining timely actionable intelligence is embedding intelligence at the source of the sensing. This development enables decisions to be made at the sensor rather than “phoning home” to headquarters or a cloud service for “what to do next”.
Since many IoT applications have operational control, making decisions quickly is essential. Unfortunately, the latency inherent in data processing and decision support far from the edge is too slow for many applications.
In some cases, IoT devices must be controlled within milliseconds by intelligence that resides within the control loop. A good example is military aircraft, where sensor data needs to be acted upon constantly, on the spot. If the thousands of sensors were wired to a central computer onboard the aircraft, the wiring and the computer could weigh more than the entire aircraft. This necessitates the use of edge computing architectures and equipping smart IoT devices with artificial intelligence.
AI on a Chip Unlocks Intelligence to Operate Complex Systems and Makes it Tamper Proof
Today, 25% of organizations with established IoT strategies are also investing in AI. However, what commonly passes as “AI” these days – conventional software approaches designed to handle very large, complex data sets, or chatbots that possess rudimentary contextual awareness – are not sufficient.
Some chip companies are working on incorporating AI software on their chips. One software company, Beyond Limits, is doing the reverse: building advanced cognitive AI that can be embedded in off-the-shelf inexpensive chips. When edge devices are equipped with cognitive intelligence and are able to act without moving all the data to remote data centers for analysis, the number and type of new smart IoT applications is virtually limitless.
Consider These Real-World Industrial Applications
• Mesh networks, such as swarms of industrial drones or remote facilities filled with smart sensors and actuators, need to be able to communicate and coordinate with each other to accomplish tasks without being connected to a mothership.
• An offshore oil rig may have thousands of sensors that need internet connectivity to provide data to the land base. Meanwhile, rigs don’t have fast internet connections, so cloud service AI is not feasible.
• In the energy industry, determining where to site a well is an expensive decision. Expert human decision-making must be augmented and backed up by data from geological, geospatial, seismic, weather, historical production performance, and subsurface sensors that must operate in remote areas and under extreme conditions.
Cognitive AI: Intelligence at the Edge
Cognitive artificial intelligence – truly intelligent symbolic AI software with bio-inspired, human-like reasoning ability – will take IoT technologies to the next level and allow enterprises to make full use of their IoT investments. Using cognitive AI, IoT devices can work together to not only analyze time-sensitive data at the point of origin but also diagnose and solve problems in real-time, even when the devices cannot communicate with their operators.
More futuristic applications of cognitive AI in the IoT sphere include:
• Currently, large fleets of ships are largely unmonitored and un-instrumented, especially compared to other modes of transportation, such as jets and smart cars. If we imagine supertankers as nothing more than larger-than-average IoT devices, they can be connected, tracked, and coordinated through networks of satellites powered by cognitive AI.
• Cognitive AI can take smart medical devices to the next level by making it possible for two completely independent systems – one inside the body, the other attached to the body – to work in sync. Imagine an intelligent spinal alignment implant that communicates with an intelligent prosthetic limb to coordinate strategies to give a patient balance, allowing them to walk confidently, increasing their stamina and reducing their strain, all while providing actionable data to physical therapists. Currently, IoT medical devices are not much more than dynamic alarm limits.
Sounds like science fiction? These scenarios are not so different from empowering semi-autonomous rovers on Mars that make decisions, from great distances and in the most extreme conditions imaginable – which has already been done by Caltech and NASA’s Jet Propulsion Laboratory. In 2012, AI technology was used to support the landing of the Curiosity Rover on Mars and operate it 150 million miles from Earth. Advanced AI technology has also been employed by NASA to monitor the Voyager 2 deep space probe and search for water on Mars.
The vision of the future for AI includes cognitive systems that can do what machine learning systems can’t: intelligently and fluently interact with human experts and provide articulate explanations and answers, even at the edge of the network. Across the board, you will see, and work with, systems endowed with rare and valuable intelligence.
About the Author:
AJ Abdallat is CEO of Beyond Limits, an artificial intelligence and cognitive computing company that is transforming proven space and defense technology from NASA and the U.S. Department of Defense into innovative solutions to address large and emerging markets.