Autonomous Cars Could Eventually Cut 25,000 Jobs Per Month

The automotive industry is growing. From limited assistance to piloting to full automation, car makers strive to limit human intervention. But this promising industry could cause significant job losses worldwide and in the United States in particular.

Independent vehicles have advantages, such as helping the elderly or disabled or avoiding road accidents caused by human error. On the other hand, like automation in general, there will be job losses, and those resulting from autonomous cars can be very important. This is what a new report on the subject, realized by Goldman Sachs, affirms.

According to the report, The Americans will find themselves with a job loss of 25,000 per month, or 300,000 jobs a year, because of semi-autonomous and self-employed cars. The primary victims of this automation will be truck drivers more than any other professional driver, accor

The automotive industry is growing. From limited assistance to piloting to full automation, car makers strive to limit human intervention. But this promising industry could cause significant job losses worldwide and in the United States in particular.

Independent vehicles have advantages, such as helping the elderly or disabled or avoiding road accidents caused by human error. On the other hand, like automation in general, there will be job losses, and those resulting from autonomous cars can be very important. This is what a new report on the subject, realized by Goldman Sachs, affirms.

According to the report, The Americans will find themselves with a job loss of 25,000 per month, or 300,000 jobs a year, because of semi-autonomous and self-employed cars. The primary victims of this automation will be truck drivers more than any other professional driver, according to the report. It should be noted that among the 4 million professional drivers in the United States in 2014, there were 3.1 million truckers.

If Goldman Sachs believes that the full impact of autonomous cars is several decades ahead of us, society recognizes that when it happens, such a number of jobs will be lost in the United States. However, the report states that regulation and slower adoption can delay these effects.

Goldman Sachs estimates that sales of semi-autonomous and autonomous cars should already account for 20% of the total sale of cars between 2025 and 2030. A percentage justified by the profound changes put in place by the manufacturers for more automation.

The official launch of Uber Freight recently and the CEO of Ford who will be replaced by an independent car expert reflect the policies of these manufacturers for the future. But the automation of cars is only a stage of global automation that threatens other professionals like secretaries, cashiers, bank teller, waiters, and realtors.

Other industries such as retail, telecommunications, printing, and publishing have already lost a lot of jobs over the last decade. On the other hand, the sectors of food services, education, computer design or home care seem the best survivors of this wave of automation, according to the report.

OpenAI Designs an AI-Based Algorithm That Allows a Robot to Mimic Tasks Performed by Humans

In December 2015, Elon Musk and some people and companies in the technology industry joined forces to announce the creation of OpenAI, a non-profit organization with the goal of making the results available worldwide Research in the field of artificial intelligence without requiring financial compensation.

At the time of its creation, the founders of the company explained that their researchers will be strongly encouraged to publish their work in the form of documents, blog posts, code, and patents (if any) World. A few years have now passed, and a few days ago, the company announced the availability of a new algorithm based on artificial intelligence.

OpenAI has announced the availability of a framework allowing robots to learn by imitating what they are given to see. Generally, for a system to be able to master the various facets of a task and run it without

In December 2015, Elon Musk and some people and companies in the technology industry joined forces to announce the creation of OpenAI, a non-profit organization with the goal of making the results available worldwide Research in the field of artificial intelligence without requiring financial compensation.

At the time of its creation, the founders of the company explained that their researchers will be strongly encouraged to publish their work in the form of documents, blog posts, code, and patents (if any) World. A few years have now passed, and a few days ago, the company announced the availability of a new algorithm based on artificial intelligence.

OpenAI has announced the availability of a framework allowing robots to learn by imitating what they are given to see. Generally, for a system to be able to master the various facets of a task and run it without problems, it requires learning tests on a broad range of samples. OpenAI, therefore, wanted to go even faster in learning by allowing robots to learn as human beings do.

This gave rise to the “one-shot imitation learning” framework. With this algorithm, a human can communicate to a robot how to perform a new task after executing it in a virtual reality environment. And from a single demonstration, the robot can perform the same task from an arbitrary initial configuration.

Thus one can construct a policy by learning imitation or reinforcement to stack blocks in towers of 3. But with this new algorithm, researchers have succeeded in designing policies that are not specific to a particular task, but rather can be used by a robot to know what to do in a new situation of a task.

In the above video, OpenAI has a demonstration of the formation of a policy that solves a different instance of the same task with as a learning data the simulation observed on another demonstration.

To stack the blocks, the robot uses an algorithm supported by two neural networks, namely a vision network and an imitation network. The vision array acquires the desired capabilities by recording hundreds of simulated images in a task with different lighting, texture, and object disturbances. The imitation network observes a demonstration, milking, reduces the trajectory of the moving objects and then accomplishes the intention starting with blocks arranged differently.

Below the imitation network, it has a process called “Soft Attention” that deals with both the different steps and actions as well as the appropriate blocks to be used in stacking and also the components of the vector specifying the locations of the various blocks in the environment.

The researchers explain that for the robot to learn a robust policy, a modest amount of noise has been introduced into the results of the script policy. This allowed the robot to perform its task properly even when things go wrong. Without the injection of this noise, the robot would not have been able to generalize what he learned by observing a specific task.

Finally, it should be noted that although the “one-shot imitation learning” algorithm was used to teach a robot to move blocks of colored cubes, it can also be used for other tasks.

Artificial Intelligence: Friend or Enemy of Cybersecurity?

Security strategies must undergo a radical revolution. Tomorrow’s security devices will need to see and operate internally among them to recognize changes in the interconnected environments and thus automatically be able to anticipate risks, update and enforce policies.

Devices must have the ability to monitor and share critical information and synchronize their responses to detect threats.

Sounds very futuristic? Not really. A new technology that has recently grabbed attention lays the foundation for such an automation approach. This has been called Intent-Based Network Security (IBNS).

This technology provides extended visibility across the entire distributed network and enables integrated security solutions to automatically adapt to changes in network configurations a

Security strategies must undergo a radical revolution. Tomorrow’s security devices will need to see and operate internally among them to recognize changes in the interconnected environments and thus automatically be able to anticipate risks, update and enforce policies.

Devices must have the ability to monitor and share critical information and synchronize their responses to detect threats.

Sounds very futuristic? Not really. A new technology that has recently grabbed attention lays the foundation for such an automation approach. This has been called Intent-Based Network Security (IBNS).

This technology provides extended visibility across the entire distributed network and enables integrated security solutions to automatically adapt to changes in network configurations and change needs with a synchronized response against threats.

These solutions can also dynamically divide network segments, isolate affected devices, and get rid of malware. Similarly, new security measures and countermeasures can be automatically upgraded as new devices, services, and workloads are moved or deployed to and from anywhere in the network and from devices to the cloud.

The tightly integrated automated security allows for a general response against threats far greater than the total of all individual security solutions that protect the network.

Artificial intelligence and machine learning have become significant allies for cybersecurity. Mechanical learning will be reinforced by devices packed with information from the Internet of Things and by predictive applications that help to safeguard the network. But securing those “things” and information, which are ready targets or entry points for cybercriminals, is a challenge in itself.

The quality of intelligence

One of the greatest challenges of using artificial intelligence and machine learning lies in the caliber of intelligence. Today, Intelligence against cyber threats is highly prone to false positives due to the volatile nature of IoT.

Threats can change in a matter of seconds; one device can be flushed out, infect the next and then re-emptied back into a full low latency cycle.

Improving the quality of intelligence against threats is extremely important as IT teams increasingly transfer control to artificial intelligence to perform work that they otherwise should do. This is an exercise in trust, and this is a unique challenge.

As an industry, we can not transfer total control to an automated device, but we need to balance operational control with essential execution that can be performed by the staff. These work relationships will really make artificial intelligence and machine learning applications for cyber defense really effective.

Because there is still a shortage of talent in cybersecurity, products and services must be developed with greater automation in order to correlate intelligence against threats and thus, determine the level of risk to synchronize a coordinated response automatically.

By the time managers try to tackle a problem on their own, it is too late, even causing a major problem or generating more work. This can be handled automatically, using a direct exchange of intelligence between detection and prevention products or with assisted mitigation, which is a combination of people and technology working together.

Automation also allows security teams to allocate more time to the business goals of the company, rather than spending time in the routine administration of cybersecurity.

In the future, artificial intelligence in cybersecurity will constantly adapt to the growth of the attack surface. Today, we are barely connecting points, sharing information and applying that information to systems.

People are making these complex decisions, which require a correlation of intelligence from humans. It is expected that in the coming years, a mature artificial intelligence system may be able to make complex decisions for itself.

What is not feasible is total automation; That is, transfer 100% of the control to the machines so that they make the decisions all the time. People and machines must work together.

The next generation of “conscious” malware will use artificial intelligence to behave like a human, perform reconnaissance activities, identify targets, choose attack methods, and intelligently evade detection systems.

Just as organizations can use artificial intelligence to improve their security posture, cybercriminals can also start using it to develop smarter malware.

It guided by offensive intelligence set and analysis such as the types of devices deployed in the segment of a network, traffic flow, applications being used, transaction details or the time of day in which they occur.

The longer a threat remains within the network, the greater the ability to operate independently, to blend into the environment, to select tools based on the target platform, and eventually to take countermeasures based on the security tools found in the place.

This is precisely the reason why an approach is needed where security solutions for networks, accesses, devices, applications, data centers and cloud work together as an integrated and collaborative system.

With Artificial Intelligence, The Travel Industry Can Better Understand its Customers

If you have heard about artificial intelligence (AI) and are updated on recent developments, you may have heard of these terms: deep learning, algorithms, machine learning, etc. As we progress in the flowering of AI, Deep Learning is implemented in various industries and platforms, and we begin to see tangible applications in our daily lives.

It is a fact that the travel industry has much to improve: business travel can turn into nightmares easily, travelers are even more demanding when choosing their travel option and expect full-time care and support.

Artificial Intelligence is already common to most travelers in the form of computer helpers like Ok Google, Siri and Cortana, and technologies like this can respond to current problems.

The abundance of data that travel organizations have, including traveler profiles, action history, airline preferences, and hotels, make the business of the tourism easily appropriated to the AI. Companies such as <a href="https://www.poder.io/&quot;

If you have heard about artificial intelligence (AI) and are updated on recent developments, you may have heard of these terms: deep learning, algorithms, machine learning, etc. As we progress in the flowering of AI, Deep Learning is implemented in various industries and platforms, and we begin to see tangible applications in our daily lives.

It is a fact that the travel industry has much to improve: business travel can turn into nightmares easily, travelers are even more demanding when choosing their travel option and expect full-time care and support.

Artificial Intelligence is already common to most travelers in the form of computer helpers like Ok Google, Siri and Cortana, and technologies like this can respond to current problems.

The abundance of data that travel organizations have, including traveler profiles, action history, airline preferences, and hotels, make the business of the tourism easily appropriated to the AI. Companies such as Poder.IO, specializing in AI solutions for sectors such as travel, are not only architects but also witnesses of what airlines, hotels, and companies associated with tourism are creating to serve travelers differently before, during and after their travels.

An example is Pana, a specialist on-demand travel company, contacts its customers using messages through applications, SMS or email. It consolidates the regular management of the local dialect, with information on the inclinations of the traveler, and uses the AI to indicate to the operators the decisions that more applications in the middle of the reservation procedure.

The KLM airline allows its travelers to obtain reservations confirmations, registration notices, tickets and flight announcements through a Facebook Messenger bot. They can also contact KLM through Messenger all day, every day.

And the Hilton hotel chain is testing Connie, a robotic assistant, powered by IBM Watson and Wayblazer. It can answer questions from visitors regarding courtesies, management, and nearby attractions. The more visitors you connect with Connie, the more you learn, adjust, and improve your suggestions and responses.

On the other hand, Customer relationship management in the travel industry is always about information and AI also helps in this regard. To build reliable relationships with customers and travelers, tourism managers need to know a lot about their customers; this information covers everything from their age, sexual orientation, gastronomic preferences, and interests.

Most of these data can be used throughout the client’s journey to maintain service at phenomenal levels, and be present to throw carrots at the right time to maintain their loyalty. Artificial intelligence can make this happen with just a few clicks.

The AI brings to the light valuable knowledge that travel managers had never conceived possible. This, in principle, should lead to greater customer benefit, better quality advertising and increased loyalty to brands that use their data in the right way.

That is why obtaining the necessary information and releasing the right messages for the right travelers is the biggest challenge for the tourism industry.

SoftBank Wants to Become a Giant of Robotics and Artificial Intelligence

The Japanese company SoftBank will buy two robotics companies owned by Google. Contrary to what its name suggests, the Japanese company SoftBank is not a bank but a telecommunications company. Softbank loves technology and especially robots.

In 2015, it bought the French Aldebaran (Nao, Pepper). It has just announced the acquisition of two US robotics companies owned by Alphabet, the parent company of Google, Schaft and Boston Dynamics.

Robots for military use

Schaft and Boston Dynamics specialize in bipedal and quadruped mobile robots. They already have several astounding creatures like Atlas, a kind of Robocop standing on two legs; Spot, a big dog robot that always falls on its paws and can carry military equipment or Handle, a robot on wheels that jumps to 1.2 meters high.

All this would be even a bit creepy. This would be partly because it gives a disturbing picture of the Google Galaxy, which Alphabet would

The Japanese company SoftBank will buy two robotics companies owned by Google. Contrary to what its name suggests, the Japanese company SoftBank is not a bank but a telecommunications company. Softbank loves technology and especially robots.

In 2015, it bought the French Aldebaran (Nao, Pepper). It has just announced the acquisition of two US robotics companies owned by Alphabet, the parent company of Google, Schaft and Boston Dynamics.

Robots for military use

Schaft and Boston Dynamics specialize in bipedal and quadruped mobile robots. They already have several astounding creatures like Atlas, a kind of Robocop standing on two legs; Spot, a big dog robot that always falls on its paws and can carry military equipment or Handle, a robot on wheels that jumps to 1.2 meters high.

All this would be even a bit creepy. This would be partly because it gives a disturbing picture of the Google Galaxy, which Alphabet would be willing to get rid of these two companies. Schaft and Boston Dynamics are working with the US military, and the US government has yet to agree to the buy-in.

Behind SoftBank, a visionary

Behind SoftBank, the CEO, Masayoshi Son, is fond of investing in new emerging technologies. He regularly visits Silicon Valley and claims to have been a friend of Steve Jobs.

Masayoshi Son has a vision. For now, SoftBank does not really innovate but puts the package on the acquisition of innovative companies because it wants to become the biggest technology investor in the world. Last year, the group joined forces with Saudi Arabia to create the world’s largest technology investment fund.

SoftBank wants to become a global player in robotics and artificial intelligence. Masayoshi Son believes much in the “technological singularity,” the rather controversial idea that artificial intelligence will one day exceed human intelligence.¬†SoftBank Robotics is in any case undeniably a company to follow closely.

This Train Does Not Need Any Rails

Sensors, which are based on a white dashed line and the autonomous Rapid Rail Transit in China safely travel on that line. The train can 300 passengers and has space in the combination of bus and train.

China is one of the most developed countries in the world. The engineers are especially interested in innovations in local transport. Now a combination of bus and train was presented, which can drive autonomously. Autonomous Rail Rapid Transit (ART) is the new means of transport, as the state news website Xinhua.net reports.

The CRRC Zhuzhou Locomotive Co., Ltd., is about 30 meters long and can carry more than 300 passengers. After a 10-minute recharge time, the “Smart Bus” can travel 25 kilometers with a speed of up to 70 kilometers per hour.

[youtube https://www.youtube.com/watch?v=Dd3N9CFKe9M%5D

In

Sensors, which are based on a white dashed line and the autonomous Rapid Rail Transit in China safely travel on that line. The train can 300 passengers and has space in the combination of bus and train.

China is one of the most developed countries in the world. The engineers are especially interested in innovations in local transport. Now a combination of bus and train was presented, which can drive autonomously. Autonomous Rail Rapid Transit (ART) is the new means of transport, as the state news website Xinhua.net reports.

The CRRC Zhuzhou Locomotive Co., Ltd., is about 30 meters long and can carry more than 300 passengers. After a 10-minute recharge time, the “Smart Bus” can travel 25 kilometers with a speed of up to 70 kilometers per hour.

In addition, ART does not need any rails. Instead, it is based on built-in sensors only on a white dashed line on the ground. This is also the decisive advantage of this new type of track.

The construction of a metro including rails costs between 400 and 700 million yuan (52.29 million and 91.5 million euros) per kilometer. The construction of an ART costs only about 15 million yuan (1.96 million euros).

“There is a huge demand for urban transit systems,” the company said. One reason for this is the steadily growing urban population. The first real use of ART is planned in 2018 on a 6.5 kilometer stretch in Zhuzhou, north of Hong Kong.

Google is Now Measuring Air Pollution in the US Cities

Google has used its Street View vehicles to measure air pollution in US cities. The first result of the study was the detailed maps of Oakland, California, where the dioxin and CO2 exposure is highest.

The cars of Google Street View can do more than just take shots from the streets of this world. In Oakland, California, Google has used its vehicles for the study High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data to capture air pollution in the 400,000-inhabitant city accurately.

[youtube https://www.youtube.com/watch?v=mFnE8r0RoYg%5D

For the project, Google collaborated with the University of Texas at Austin, the NGO Environmental Defense Fund (EDF), and the company Aclima. Between May 2015 and May 2016, street-view cars drove a

Google has used its Street View vehicles to measure air pollution in US cities. The first result of the study was the detailed maps of Oakland, California, where the dioxin and CO2 exposure is highest.

The cars of Google Street View can do more than just take shots from the streets of this world. In Oakland, California, Google has used its vehicles for the study High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data to capture air pollution in the 400,000-inhabitant city accurately.

For the project, Google collaborated with the University of Texas at Austin, the NGO Environmental Defense Fund (EDF), and the company Aclima. Between May 2015 and May 2016, street-view cars drove a total of more than 25,000 kilometers across Oakland and collected specific airborne data in the metropolitan area using special sensors. Typically, such data is determined with fixed stations, and Google vehicles have been given the opportunity to work much more precisely thanks to their mobility.

During the study period, the vehicles rolled an average 30 times over each street in Oakland, resulting in a total of 2.7 million measurement points. Google generated an interactive Google Maps card that the EDF published on its website.

google_maps_pollution

The environmental organization shows which streets the exposure to CO2 or nitrogen is the highest. As you would expect, the values on and in the vicinity of Highways are the highest as heavy trucks drive along the way.

High buildings also ensure that people are exposed to health risks because the fresh air exchange is disturbed in such areas. Due to the high levels of air pollution, there are twice as many asthmatics in Oakland as Alameda County, also located in the San Francisco Bay Area.

According to a blog post from Aclima, the study was a pilot project to be expanded in the future. The company, which offers sensors and cloud solutions among other things, reached an agreement with Google in September 2015 that the street-view vehicles in other US cities should measure the air pollution.

To date, the converted cars have driven nearly 130,000 kilometers through California to collect data for further environmental studies in San Francisco and Los Angeles. They are to be published in the coming months.