Who should be held legally responsible when a self-driving car hits a pedestrian? Should the finger be pointed at the car owner, manufacturer or the developers of the artificial intelligence (AI) software that drives the car?

Struggles to assign liability for accidents involving bleeding-edge technologies like AI have dominated global conversations even as trials are ongoing in many places around the globe including in Singapore, South Korea and Europe.

During the third annual edition of the TechLaw.Fest forum last Wednesday, panellists said that Singapore’s laws are currently unable to effectively assign liability in the case of losses or harm suffered in accidents involving AI or robotics technology.

“The unique ability of autonomous robots and AI systems to operate independently without any human involvement muddies the waters of liability,” said lawyer and Singapore Academy of Law Robotics and Artificial Intelligence Sub-committee co-chair Charles Lim.

He was speaking in a webinar titled “Why Robot? Liability for AI system failures”.

The 11-member Robotics and Artificial Intelligence Sub-committee published its report on what can be done to establish civil liability in such cases last month.

“There are multiple factors (in play) such as the AI system’s underlying software code, the data it was trained on, and the external environment the system is deployed in,” he said.

For example, an accident caused by a self-driving car’s AI system could be due to a bug in the system’s software, or even an unusual situation such as a monitor lizard crossing the road that the system has not been trained to recognise.

A human’s decisions could still influence events leading up to an accident too, as existing self-driving cars have a manual mode that allows a human driver to take control.

A software update applied by the AI system’s developers could also introduce new, unforeseen bugs.

This makes retracing every step of an AI system’s decision-making process to prove liability a complex and costly task, Mr Lim said.

Fellow panellist and robotics and AI sub-committee member Josh Lee said a first step for lawmakers here could be to figure out what to call the person behind the wheel.

“We propose that the person be called a ‘user-in-charge’, because he or she may not be carrying out the task of driving, but retains the ability to take over when necessary,” he said. “This can have application beyond driverless vehicles… in many scenarios today, such as medical diagnosis.”

The term “user-in-charge” was first mooted by the United Kingdom Law Commission in 2018, where proposals were tabled to put such users in a fully automated environment under a separate regulatory regime.

AI and robotics sub-committee member and lawyer Beverly Lim said during the webinar that the one person who should not be held liable is the driver behind the wheel, because he would have bought the vehicle thinking the AI would be a comparatively more reliable driver.

But the biggest issue arising from liability involving robotics and AI is ensuring that victims will be able to get compensation.

“For example, how are they supposed to prove (liability) when they don’t have access to the data, or might not understand it?” said Ms Lim.

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Driverless truck startup Starsky Robotics is shutting down, but not before sharing some cold hard truths about the autonomous driving industry. Founded in 2015, Starsky proposed a combination of self-driving and remote control for a fleet of next-generation trucks, saving full autonomy for the highway.

Rather than build a driverless truck that could handle every situation, Starsky’s plan was to mix autonomous systems with human teleoperation. On the highway, a relatively controlled environment, the truck would drive itself. That way, the demands for skilled operators would be significantly reduced.

In trickier situations, however – effectively “the first and last mile,” as Starsky explained it – a human driver would take over the controls. They wouldn’t be physically present in the truck, mind. Instead they’d use remote controls to pilot the rig from a distance.

Back in 2019, Starsky demonstrated the first fully-unmanned truck to drive on a live, public highway. Now, though, the company is shutting down. In a blunt post-mortem of what went wrong, founder and CEO Stefan Seltz-Axmacher blamed results-hungry investors, unexpected difficulties with getting the AI right, and the fact that safety just isn’t sexy for Starsky’s problems – and the problems that he predicts will impact the self-driving industry as a whole.

“There are too many problems with the AV industry to detail here,” Seltz-Axmacher writes, “the professorial pace at which most teams work, the lack of tangible deployment milestones, the open secret that there isn’t a robotaxi business model, etc. The biggest, however, is that supervised machine learning doesn’t live up to the hype. It isn’t actual artificial intelligence akin to C-3PO, it’s a sophisticated pattern-matching tool.”

The issue, he explains, is that matching – and eventually exceeding – human drivers’ abilities with edge cases is much tougher than most realized. Everyday driving in reasonable conditions is fairly low-hanging fruit; that can be achieved relatively rapidly. Developing a system that is capable of reacting safely to unexpected situations, however, is far trickier, and as you refine the self-driving AI you also set yourself the challenge of finding increasingly specific risk models with which to test.

Adding to that problem is the fact that, while safety is often cited as a primary concern for people when asked about whether they’d get into an autonomous vehicle, it’s actually a tough thing to get people excited about. The same, Seltz-Axmacher says, goes for investors. Starsky spent almost two years working on safety engineering, but “the problem is that all of that work is invisible,” he writes.

“Investors expect founders to lie to them,” the Starsky founder explains, “so how are they to believe that the unmanned run we did actually only had a 1 in a million chance of fatality accident? If they don’t know how hard it is to do unmanned, how do they know someone else can’t do it next week?”

At the end of 2019, the company’s attempts to raise more money fell flat. It’s currently seeking to sell off its patents as the company breaks apart. Seltz-Axmacher says he sees real autonomy still being 10 years out; “no one should be betting a business on safe AI decision makers,” he concludes.

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Chinese autonomous vehicle startup Pony.ai said Wednesday that it has received a $400 million investment from Japanese automaker Toyota, upping the ante in its push to develop automated vehicles.

The Guangzhou-based company will use the fresh capital to boost the development and commercialization of driverless cars, which will mainly feature what’s called “Level 4 autonomy.”

The Society of Automotive Engineers divides autonomous driving technology into six levels ranging from 0 to 5. Level 4 autonomy allows a car to be in almost total control all the time without any human intervention.

The new investment marks an extension of the two companies’ partnership initially forged in August 2019, when Pony.ai and Toyota joined forces to conduct road tests of autonomous vehicles in Beijing and Shanghai using the Japanese automaker’s Lexus-branded cars.

According to a statement published on Pony.ai’s public WeChat account, the two partners will also explore a possible partnership on mobility services.

In 2018, Pony.ai started piloting its PonyPilot project in the southern city of Guangzhou, testing a fleet of 100 autonomous cars used for taxi-hailing.

Toyota is also a financial backer of Chinese ride-hailing giant Didi Chuxing. In July last year, the Japanese carmaker announced that it would invest $600 million in Didi and their new joint venture for mobility services. Didi also plans to test its robotaxi services in Shanghai this year.

Earlier this week, China’s National Development and Reform Commission and several other government agencies published an autonomous vehicle development plan, setting a goal for 2025, by which time the country should achieve mass production of vehicles with “conditional” self-driving capabilities.

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Gareth Coltman, global head of automation at MarketAxess, the electronic platform for fixed income trading and reporting, said the industry is going through a second revolution which will lead to radical changes in market structure.

Coltman told Markets Media: “The first revolution in electronic trading involved automating request for quotes and Open Trading. We are seeing the second revolution in electronic trading which has been enabled by the pre-trade data provided by CP+ and will lead to radical changes in market structure.”

Open Trading, MarketAxess’ all-to-all trading protocol, allows multiple parties in a network to come together to trade, rather than the traditional model of only banks supplying liquidity to the buy side.

Composite+ (CP+)

CP+ is the firm’s algorithmic pricing engine that uses artificial intelligence to price corporate bonds using a variety of data sources including public reports and proprietary MarketAxess data.

David Krein, global head of research at MarketAxess, told Markets Media that the firm experimented with machine learning for 18 months before the launch of CP+ in May 2017, and found it clearly made better sense of the data.

“After searching for tools to speed up and improve our process, we decided to use H20.ai to implement the necessary algorithms,” he added.

H2O.ai said in a statement last month that its platform provides open source artificial intelligence and machine learning capabilities to MarketAxess’ CP+.

The pricing engine’s algorithm consumes more than 200 features and produces an unbiased, two-sided market for 95% of the tradable universe which is updated every 15 to 60 seconds, depending on the liquidity of the instrument.

“The predicted prices of CP+ track traded levels very closely, and we aim for zero average difference between the two,” said Krein. “A real-time accurate pre-trade reference price for corporate bonds has not been available before.”

Sri Ambati, chief executive and founder at H2O.ai, told Markets Media that the firm’s open source platform can perform one billion regressions in less than five seconds.

“This ensures data is correct in rapidly changing markets, which is very powerful when combined with MarketAxess’s domain knowledge in fixed income,” said Ambati.

The technology provider also has a H2O Driverless AI platform which uses automation to accomplish tasks including model validation, model selection and deployment and machine learning interpretability much more quickly.

Ambani explained that testing algorithms automatically allows firms to “fail faster.” He added: “By testing with more rapid iterations the right strategy can be found more quickly.”

Krein continued that artificial intelligence has been used in CP+ for euro bonds and emerging markets globally.

“We have already extended the facility into eight local emerging markets, where data is hardest to come by, making a pricing tool such as CP+ that much more valuable,” he added.

Electronic trading 

In the US, the outperformance of electronic trading can be evidenced by comparing venue transactions to Trace, the reporting system.

Krein said: “In other markets, we have used CP+ as a benchmark as well and we can now observe, measure, and track the same results.”

MarketAxess has constructed trade performance indexes against Trace as a benchmark. The MarketAxess U.S. Investment Grade Trading Performance Indexes give a view of the relative “over-performance” or “under-performance” associated with venue selection.

When the indexes were launched in April last year MarketAxess said in a statement that participants had an estimated improvement of 0.9 basis points in yield per bond in March 2019 when trading US Investment Grade credit on the MarketAxess platform versus trading away.

“We will be rolling it out to other markets in the coming months,” Krein added.

Automation

Coltman said: “Fixed income currently has automation of traditional RFQs but new tools and protocols are emerging which will impact all trading activity.”

For example, MarketAxess launched Live Markets for new issues and portfolio trading six months ago. Live Markets is a protocol for Open Trading which creates a single view of two-way, actionable prices for the most active bonds. In addition, executing in Live Markets gives evidence of best execution as the platform provides details of the quotes in the market at that point in time.

Coltman continued that transactions are ripe for automation and adoption will soon become ubiquitous in fixed income.

“We only launched automated execution two years ago and already between 50% and 60% of activity for some of our biggest clients is auto-executed,” he added. “The industry does not realise how fast this is happening.”

AI in financial services

Ambani said: “We are in the earliest days of the AI era in financial services.”

In December last year Credit Suisse selected H2O.ai as a member of its 2019 Disruptive Technology Recognition Program. The scheme is a joint initiative between the bank’s investment banking and capital markets division and the group chief technology officer function.

Ambati said: “H2O.ai was honored to be selected into the coveted Credit Suisse’s DTR program to partner across every group within the bank and co-invent AI.”

The partnership with the bank began when Credit Suisse adopted H2O Open Source in core finance and banking. Subsequently in 2019 the bank chose H2O Driverless AI to accelerate AI adoption in front-office and back-office in global markets, fixed income and capital markets.

In August last year another bank, Goldman Sachs, led a $72.5m series D ending in H2O.ai alongside the Ping An Global Voyager Fund,  which took total funding to $147m.  Jade Mandel from Goldman Sachs joined the H2O.ai board.

Erdit Hoxha, head of European equity trading at Goldman Sachs Securities Division, said in a statement at the time: “The results we’ve got with H2O are promising, we are now looking at wider adoption of the AI models across the equity trading floor for market making.”

Ambati continued that H2O.ai was founded in 2012 with the aim of democratizing AI for everyone.

“We want to provide signal sharing as a service and intelligence as a service,” he added. “The democratization of technology and talent will transform the whole capital markets industry and lead to more transparency and easier access to capital.”

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FROM cognitive intelligence, in-memory-computing, fault-tolerant quantum computing, new materials-based semiconductor devices, to faster growth of industrial IoT, large-scale collaboration between machines, production-grade blockchain applications, modular chip design, and AI technologies, we can expect technology advancements and breakthroughs to gain momentum and generate a great impact on our daily lives in the year ahead.

Here are the top 10 technology trends for 2020, as seen by the Alibaba Damo Academy, Alibaba Group’s global research initiative.

Artificial intelligence evolves from perceptual intelligence to cognitive intelligence

Artificial intelligence has reached or surpassed humans in the areas of perceptual intelligence such as speech to text, natural language processing, video understanding etc; but in the field of cognitive intelligence that requires external knowledge, logical reasoning, or domain migration, it is still in its infancy.

Cognitive intelligence will draw inspiration from cognitive psychology, brain science, and human social history, combined with techniques such as cross domain knowledge graph, causality inference, and continuous learning to establish effective mechanisms for the stable acquisition and expression of knowledge. These make machines understand and utilise knowledge, achieving key breakthroughs from perceptual intelligence to cognitive intelligence.

In-Memory-Computing addresses the “memory wall” challenges in AI computing

In Von Neumann architecture, memory and processor are separate and the computation requires data to be moved back and forth. With the rapid development of data-driven AI algorithms in recent years, it has come to a point where the hardware becomes the bottleneck in the explorations of more advanced algorithms.

In Processing-in-Memory (PIM) architecture, in contrast to the Von Neumann architecture, memory and processor are fused together and computations are performed where data is stored with minimal data movement. As such, computation parallelism and power efficiency can be significantly improved. We believe the innovations on PIM architecture are the tickets to next-generation AI.

Industrial IoT powers digital transformations

In 2020, 5G, the rapid development of IoT devices, cloud computing and edge computing will accelerate the fusion of information, communications, and industrial control systems. Through advanced Industrial IoT, manufacturing companies can achieve automation of machines, in-factory logistics, and production scheduling, as a way to realise C2B smart manufacturing.

In addition, interconnected industrial systems can adjust and coordinate the production capability of both upstream and downstream vendors. Ultimately it will significantly increase the manufacturers’ productivity and profitability.

Large-scale collaboration between machines becomes possible

Traditional single intelligence cannot meet the real-time perception and decision needs of large-scale intelligent devices. The development of collaborative sensing technology of between the Internet of Things and 5G communication technology will realise the collaboration among multiple agents — machines cooperate and compete with each other to complete target tasks.

The group intelligence brought by the cooperation of multiple intelligent bodies will further amplify the value of the intelligent system: large-scale intelligent traffic light dispatching will realise dynamic and real-time adjustment, while warehouse robots will work together to complete cargo sorting more efficiently; driverless cars can perceive the overall traffic conditions on the road, and group unmanned aerial vehicle (UAV) collaboration will get through last -mile delivery more efficiently.

Modular design makes chips easier and faster by stacking chiplets together

Traditional chip design cannot efficiently respond to the fast evolving, fragmented and customised needs of chip production. The open source SoC chip design based on RISC-V, high-level hardware description language, and IP-based modular chip design methods have accelerated the rapid development of agile design methods and the ecosystem of open source chips.

In addition, the modular design method based on chiplets uses advanced packaging methods to package chiplets with different functions together, which can quickly customise and deliver chips that meet specific requirements of different applications.

Large-scale production-grade blockchain applications will gain mass adoption

BaaS (Blockchain-as-a-Service) will further reduce the barriers of entry for enterprise blockchain applications. A variety of hardware chips embedded with core algorithms used in edge, cloud and designed specifically for blockchain will also emerge, allowing assets in the physical world to be mapped to assets on blockchain, further expanding the boundaries of the Internet of Value and realising “multi-chain interconnection”.

In the future, a large number of innovative blockchain application scenarios with multi-dimensional collaboration across different industries and ecosystems will emerge, and large-scale production-grade blockchain applications with more than 10 million DAI (Daily Active Items) will gain mass adoption.

A critical period before large-scale quantum computing

In 2019, the race to reach “Quantum Supremacy” brought the focus back to quantum computing. The demonstration, using superconducting circuits, boosted the overall confidence on superconducting quantum computing for the realisation of a large-scale quantum computer.

In 2020, the field of quantum computing will receive increasing investment, which comes with enhanced competition.

The field is also expected to experience a speed-up in industrialisation and the gradual formation of an ecosystem. In the coming years, the next milestones will be the realisation of fault-tolerant quantum computing and the demonstration of quantum advantages in real-world problems. Either is a great challenge given present knowledge. Quantum computing is entering a critical period.

New materials will revolutionise semiconductor devices

Under the pressure of both Moore’s Law and the explosive demand of computing power and storage, it is difficult for classic Si based transistors to maintain sustainable development of the semiconductor industry.

Until now, major semiconductor manufacturers still have no clear answer and option to chips beyond 3nm. New materials will make new logic, storage, and interconnection devices through new physical mechanisms, driving continuous innovation in the semiconductor industry.

For example, topological insulators, two-dimensional superconducting materials, etc. that can achieve the lossless transport of electrons and spin can become the basis for new high-performance logic and interconnect devices; while new magnetic materials and new resistive switching materials can realise high-performance magnetics Memory such as SOT-MRAM and resistive memory.

Growing adoption of AI technologies that protect data privacy

The compliance costs demanded by recent data protection laws and regulations related to data transfer are increasing. In light of this, there has been growing interests in using AI technologies to protect data privacy.

The essence is to enable the data user to compute a function over input data from different data providers while keeping the data private. Such AI technologies promise to solve the problems of data silos and the lack of trust in today’s data sharing practices, and will truly unleash the value of data in the foreseeable future.

Cloud becomes the centre of IT technology innovation

With the ongoing development of cloud computing technology, the cloud has grown far beyond the scope of IT infrastructure, and gradually evolved into the center of all IT technology innovations.

Cloud has a close relationship to almost all IT technologies, including new chips, new databases, self-driving adaptive networks, Big Data, AI, IoT, blockchain, quantum computing and so forth.

Meanwhile, it creates new technologies, such as serverless computing, cloud-native software architecture, software-hardware integrated design, as well as intelligent automated operation.

Cloud computing is redefining every aspect of IT, making new IT technologies more accessible for the public. Cloud has become the backbone of the entire digital economy.

The post WHERE WILL TECHNOLOGY TAKE US IN 2020? appeared first on Artificial Intelligence.

Everybody in the automotive industry is hustling to progress autonomous driving technologies and deploy driverless cars. This year, we’ve seen huge steps on account of rising technologies, for example, augmented reality and artificial intelligence. In light of current circumstances, Tesla CEO Elon Musk may really have the option to make a completely autonomous vehicle before the end of 2020.

Augmented reality highlights are as of now deployed by a few automobile manufacturers and choices for aftermarket augmented reality products is developing. Because of the administrative issues of completely autonomous driving and different limitations that still should be worked through before autonomous driving innovation is widely adopted, it’s a decent bet that augmented reality will be the next automotive innovation to be seen on the roadways.

There’s a marketing challenge with autonomous driving. The general population and a significant part of the press still don’t see how it functions. What’s more, if they don’t see how it functions, they’re not liable to purchase in.

Civil Maps, a startup whose mission is to crowdsource maps for self-driving vehicles, thought of an extraordinary solution. They made sense of how to give individuals access to the mind of a self-driving machine. The innovation utilizes augmented reality to show a “vehicle’s eye view” while driving. During a ride in one of the specially-equipped cars, a screen gives the rider a visual representation of sensor information as the vehicle processes it.

The designs are unrefined, comprised of essential hues and wireframes. It would appear that a 90s form of Google Street View. However, it’s rapidly apparent exactly what amount is truly going on. With a couple of hues and some floating boxes, the demo shows how the vehicle sees the state of the street, road signs, navigational milestones, and different vehicles. It can even tell if a traffic light is red, green, or yellow. It’s an unbelievably accessible look into an extremely mind-boggling innovation.

By overlaying essential data for the driver inside their line of sight, augmented reality can improve security. Rather than a driver expecting to look down at the dash or to their phone to get driving information or data, the guarantee of augmented reality would have the information accessible on a heads-up show, the windshield or projected on the road in the driver’s line of sight. Notwithstanding assisting with route and information from gauges, augmented reality can make the driver mindful of risks and other emergency notices. Through Nissan’s Invisible-to-Visible innovation, vehicle drivers later on could pick to have augmented reality travelers in the vehicle who can cooperate with them, perform co-pilot duties, and much more.

Augmented reality’s ability to incorporate a virtual domain into this present reality makes it perfect for testing driverless cars. It gives a quicker way to deal with testing as well as an increasingly affordable one.

The University of Michigan utilizes AR and other virtual technologies to make a protected space for testing self-driving vehicles in their Mcity Test Facility. It has 32 acres of land of fake street and infrastructure. From that point, genuine vehicles can connect with computer produced autos in real-time. In this reenactment, specialists have set up various conditions and situations that mirror true challenges. This empowers them to survey the security of driverless vehicles.

Scientists likewise utilized this AR simulation to test a patent-pending programming that empowers real and virtual cars to communicate. By trading data out and about, self-driving vehicles can settle on better choices. Hence, they can explore any environment securely without imperiling the lives of their travelers. At Mcity, analysts are additionally leading tests to improve self-driving experience. Their simulations assess how drivers react to unexpected stops and different difficulties when the vehicle is accountable for the driving. In doing such, they try to improve the quality of the ride.

In comparison, a large group of smaller start-up self-driving organizations have been discreetly creating and working constrained local driverless taxi benefits in a few areas. Drive.ai, for instance, started working a self-driving shuttle service in Arlington, TX and Fresco, TX, in the territories of the city involved principally of entertainment, commercial and retail foundations. Every one of the organization’s vans is furnished with self-driving tech and can be brought utilizing an application. The project is still, in fact, a pilot, with obligatory safety drivers, yet if they keep on observing achievement it’s anything but difficult to accept that Drive.ai, or one of their rivals, could present the first wide-scale commercialized service.

Other start-up efforts incorporate a deal between Kroger markets and start-up Nuro to deliver groceries utilizing a completely autonomous vehicle called the R1, or the organization Voyage offering self-driving taxi service at a Florida retirement network called the Villages.

There’s a tremendous amount of investment in research and development of autonomous driving advancements. In any case, not many products have hit the market. Starting now, everything we can do is wait. Obviously, completely autonomous vehicles will change our everyday lives. They will disrupt enterprises as much as or maybe considerably more than augmented reality as of now has.

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Autonomous cars have been recently hitting the headlines and dominating tech-talks. It’s seen as a post-Uber disruption to public commuting and transportation of goods. It is surely not a figment of imagination in the age of artificial intelligence (AI) which is being used to complement driverless cars. The combined might of AI and driverless technologies is a formidable force.

The likes of Waymo, Tesla, etc. are heavily invested in driverless cars. In fact, Waymo has been testing the driverless car in Phoenix. Tesla has already implemented a couple of “autopiloting” features in its cars.

But before we get into further details, let us discuss what autonomous means. There are various levels of vehicle automation:

• Automation for driver assistance – It is a preliminary level or starting point of car automation where the system assists the driver but does not take control of the car. E.g. parking sensors.
• Partially automated driving – the system takes partial control, but the driver is primarily responsible for the operation of the vehicle.
• Highly automated driving – allows users to let the system take control of the vehicle for a longer duration of time. E.g. on the highway.
• Fully automated driving – The system is responsible for driving the vehicle without interference from any human. However, the human presence is still needed.
• Completely automated car – the vehicle can completely navigate its way through from a point to another without any assistance from a driver.

Depending on the level of automation, the definition of autonomous varies. While automation for driver assistance and partially automated cars are in commercial use, the remaining stages are still under test conditions.

For us to achieve the remaining stages of automation or even come close, AI is the stimulus that is being used. For the purpose of this article, let us discuss the impact of AI in highly automated driving and completely or fully automated vehicles, and how the power of AI is being harnessed to bring it to reality.

The role of artificial Intelligence in complementing the use of autonomous cars

Subject to regulatory and social acceptance, the impact of completely autonomous cars is not limited to the disruption of the public transport system. From a macro level, it impacts urbanization, township planning, food delivery, and possibly shake the ever-increasing real-estate market.

For AI to work, it needs IoT devices (such as radars, ultrasound, radar, cameras, LiDAR, accelerometers, and gyroscopes) that augment real-time operating environment and positioning of the vehicle.

Having discussed the potential of AI, let’s talk about the top four areas where AI is seen as a gamechanger towards the success of autonomous vehicles.

1. AI for self-driving car safety

Before AI completely takes over the driver’s seat, it is being used as a co-pilot to gain the confidence of the users, regulators, manufacturers. By analyzing data feeds across its sensors, AI can be handy in situations where flesh and blood drivers are prone to making human errors.

AI can score very high in areas such as:

•  Emergency control of the vehicle
  • Cross-traffic detection
  • Syncing with traffic signals
  • Breaking in cases of emergencies
  • Active monitoring of blind spots
  • Altering the driver in case he or she is distracted

The quantum of processing power needed to drive a vehicle is enormous as you do not have control of your external environment which has countless variables – it needs a lot of learning. There are numerous companies testing AI’s applicability in driving, but the most noteworthy achievements have been made by Waymo and Tesla.

Waymo’s AI algorithms are fed with real-time data from sensors, GPS, radar, lidar, cameras, and cloud services. These data are processed to produce control signals that are used to operate the car.

1. Curated cloud services targeted for individuals

AI can be used to accurately gauge the physical condition of the vehicle. Data gathered from the usage can be processed for both:

• Predictive maintenance
  Prescriptive maintenance

This way, drivers will have an easier time finding a car warranty plan that is cost-effective and meets their particular needs, and that also reflects the car’s current condition.

1. Accurate feed for regulators and insurance companies

Data from automated cars can be used to determine traffic violations and claims. From an insurance perspective, AI can be of help to determine the:

• Driver risk assessment – using AI, a driver’s behavior can be accurately gauged and based on the risk profile the premium can be charged
• Ease of claim – data from the vehicle and can be used for faster processing of claims in case of accidents. Art Financial’s AI-based video app Dingsunbao 2.0 allows users to access their auto damage.

1. Monitoring the driver and user behavior

The applicability of AI in autonomous cars is not limited to stricter requirements such as safety but also fills the fun quotient. AI can be used for a host of infotainment features in the car.

AI is helpful to provide customized infotainment during the travel. Based on the data collected over time, AI can predict and prescribe preferences based on user behavior. It could include:

• Seat position adjustment
• Mirror adjustment
• Regulating the air-conditioning
• Songs to be played

AI is gaining in prominence with each passing day. Governments too have jumped into the race to woo investors to bring AI-based driverless cars for commercial use.

In August 2018, the British Government unveiled plans for an AI simulator, intended for the purpose of attracting companies as a favorable destination for testing self-driving cars. Named as OmniCAV, the simulator can recreate a virtual version of 32km of Oxfordshire roads.

The world is changing faster than imagined, and AI is getting smarter with every passing day.We are just around the corner to witness the post-Uber era so hold your breath.

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There are many parallels between self-driving car and artificial intelligence. They are goals that, the closer we get to them, the more difficult they seem. In 1956, scientists John McCarthy and Marvin Minsky proposed that a “2-month, 10-man study of artificial intelligence” would unlock the secrets of the human mind. We would soon be able to precisely describe every aspect of learning or any other feature of intelligence in a way “that a machine can be made to simulate it.”

More than six decades later, our AI systems are still struggling to replicate the basic cognitive functions of a human child, let alone the general problem-solving capabilities of an adult mind. We never managed to create human-level AI—instead, we moved the goal posts and split AI into narrow, general and super artificial intelligence.

We can observe a similar trend in self-driving cars. A couple of years ago, the media was abuzz with the promise of self-driving cars. By many accounts, 2019 was supposed to be the year we would become used to seeing cars with empty driver’s seats on roads.

But we’re well into 2019, and self-driving cars remain an elusive target. They are still test projects in limited, fenced areas. They still make stupid mistakes. And the same people who were heralding the coming of self-driving cars are now acknowledging that it will be years before we can remove humans from behind the steering wheel.

To many, general AI and self-driving cars are impossible goals, failed projects that are only a waste of time and money. And in a sense, they might be right. We might never reach them.

But a closer look shows us that we still need people who conceive and aim for these impossible goals.

Will we ever create general AI?

General AI still has its proponents. One of the most relevant is OpenAI, the research lab founded by Elon Musk and Sam Altman that has been behind several remarkable projects. OpenAI’s mission statement reads: “OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome.”

Not everyone, however, agree with that assessment, and the our current AI technologies work very differently from the human mind.

Many of the world’s top AI scientists believe that human-level AI is a useless goal, and we already have plenty of useful narrow AI applications and technologies we should focus on instead of trying to replicate human intelligence.

One of my favorite quotes in this regard is from Peter Norvig, AI pioneer and director of research at Google. “We know how to build real intelligence—my wife and I did it twice, although she did a lot more of the work,” Norvig said in an interview with Forbes. “We don’t need to duplicate humans. That’s why I focus on having tools to help us rather than duplicate what we already know how to do. We want humans and machines to partner and do something that they cannot do on their own.”

But the real question should be, should we try to build human-level AI at all, even if it proves to be impossible? To that, I would say yes. AI’s history shows that while we are still very far from replicating the artificial version of the human brain, the process has taught us a lot about the laws of intelligence and reasoning, and has given us many other powerful tools that have augmented our abilities.

Interestingly, this brings me to another quote from Norvig. In his famous book Artificial Intelligence: A Modern Approach, Norvig says, “[W]ork in AI has pioneered many ideas that have made their way back to mainstream computer science, including time sharing, interactive interpreters, personal computers with windows and mice, rapid development environments, the linked list data type, automatic storage management, and key concepts of symbolic, functional, declarative, and object-oriented programming.”

All of these important tools would not have existed (or at least it would have taken much longer to invent them) had it not been for people who had been chasing the wild dream of thinking machines and general AI.

Likewise, when you look at all the advanced AI technologies we have today, they are strongly related to the goal of recreating the human mind. Neural networks are named and designed after the physical structure of the human brain; machine learning is inspired by the way humans learn through experience and repetition; expert systems tried to encompass the knowledge and logic of different domains in the ways that humans work.

These and many other AI techniques are not an exact recreation of their human counterparts, but they have helped solve very important problems, such as predicting cancer, detecting objects in pictures, translating text between different languages, and converting speech to text.

The quest for autonomous driving has made our roads safer

Again, we can see parallel developments in the self-driving car industry. For the moment, driverless cars are limited to test projects in areas where roads are not too busy, the weather and lighting conditions are relatively stable, and there’s a detailed digital map of the area. And wherever you see driverless cars on roads, there’s a human driver behind the steering wheel, ready to jump in if the car makes a mistake, such as accelerating toward a lane separator.

This is far from the original vision of having cars that could drive on any road, and as safely (or even safer) than human drivers.

There’s no telling when we’re going to have level-5 self-driving cars in cities, the type of driverless car that has no steering wheel and needs no human intervention at all.

But in its short history, the self-driving car industry has already delivered many important achievements. Even though we don’t have driverless cars that can operate in the open and unpredictable environments that humans are used to, we have developed plenty of useful technologies such as auto-parking, lane assist, blind-spot warnings and drowsiness detection.

These are incremental improvements, but each of them are making driving a little bit safer and will help save thousands of lives every year.

The current technology is also ready to be deployed in environments that are more controlled, such as factories and industrial facilities. An example is autonomous forklifts, which are developing into a promising market, especially since the supply of skilled operators is lagging behind the demand. There are also use cases for low-speed shuttles in low-traffic neighborhoods or inside closed complexes.

But the race to create autonomous vehicles has also brought advances in other fields, such as lidars, computer vision, sensor technology and digital mapping. These are all infrastructural technologies that have many other uses in domains that are not necessarily related to vehicles.

We still need to shoot for the stars

The history of mankind is marked with wild dreams and reality checks. The people who wanted to imitate bird flight never created flapping wings, but helped us discover the laws of flight and invent airplanes. A German scientist who was exploring telepathy never achieved his goal, but instead invented the electroencephalogram (EEG), one of the most important tools in studying the brain and diagnosing diseases related to the nervous system.

Likewise, we still need people who try to invent the impossible, whether it’s artificial general intelligence, fully autonomous vehicles or cashier-less stores. We might never reach those dreams, but what we achieve in the process is no less rewarding.

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