Artificial intelligence is among the most fascinating ideas of our time. It has captured the imagination of visionaries, science fiction writers, engineers and wall street analysts alike. In fact, artificial intelligence is in many ways a catalyst for the data revolution something that has disrupted every aspect of modern life. As with all new technologies, some are faster to embrace them, and others are much slower. Is automotive manufacturing one of the faster ones or would it be among the last?

Artificial intelligence (AI) encompasses various technologies including machine learning (ML), deep learning (neural network), computer vision and image processing, natural language processing (NLP), speech recognition, context-aware processing, and predictive APIs. But how much does this impact manufacturing and supply chain operations? Three smarts are worthy of consideration, namely smart machines, smart quality assurance and smart logistics.

The first, smart machines is relevant because improved asset utilisation is one of the greatest opportunities for AI to translate to direct savings. As overall equipment effectiveness (OEE) has been the de-facto standard to compare machine performance, automotive companies are embracing AI and machine learning (ML) algorithms to squeeze every ounce of performance from machines. Typical use cases include bottleneck detection and predictive/prescriptive maintenance. Dynamic bottleneck detection is necessary to efficiently utilise the finite manufacturing resources and to mitigate the short and long-term production constraints. In our case, we developed a neural network-based AI prediction to determine the bottleneck for the future.

A comprehensive AI strategy is vital to the success and competitiveness of automotive manufacturers, regardless of how far-fetched the use cases may seem to executives today

In terms of predictive/prescriptive maintenance, modern manufacturing machine infrastructure is designed with 3Vs for big data: volume, variability and velocity. Harnessing the potential of big data by incorporating machine learning algorithms into the data cloud, provides constant feedback to technicians and managers to ensure zero downtimes. Together with edge computing, machines are provided constant feedback based on output parameters. This leads to smarter machines that autocorrect itself based on individual cycles.

Smart quality assurance is relevant because quality controls such as quality gate are typically performed by workers. The process is often highly subjective and depends on the skill and training level of the operator. Smart assistants based on computer vision and image processing are assisting and, in some cases, taking over the inspection process. Moreover, the AI system constantly improves itself based on feedback.

The third smart is smart logistics. AI adoption in supply chains is taking off as companies realize the potential it could bring to solve their global logistic complexities, and it has a particularly significant role to play in the automotive industry.

Predictive analytics can be used to help with demand forecasting, and AI is helping network planners gain more insights on the demand patterns, resulting in improved forecasting accuracy. The efficiency gained in an accurate forecasting model has a bullwhip effect along the supply chain.

Smart warehouses are inventory systems where the inventory process is partially or entirely automated. This includes interconnected technologies to increase productivity. Smart warehouses use IIOT (Industrial Internet of Things) and AI to connect each process, data is collected at each of the nodes and the smart warehouse continuously learns and optimizes the process.

Most automakers have not taken meaningful steps towards integrating artificial intelligence in their manufacturing operations. Even the projects that do exist are mostly in partnership with universities and companies that offer products that are not customised for automotive applications.

The automotive sector, among other industries, will significantly benefit from robotic process automation (RPA) by transforming various consumer and business applications. In addition to business support functions, RPA can contribute to a number of areas in automotive manufacturing

The first movers have taken a number of initiatives (in series production, not pilot initiatives), including investments in collecting data centrally from their manufacturing operations and supply chains; projects to centrally connect a wide array of sensors to predict maintenance, uptime and other critical information using technologies such as NB-IoT; asset tracking initiatives across the supply chain; advanced predictive technologies for supply chain risks based on supplier reported KPIs and other sourced data; and investments in start-ups for predicting equipment issues.

Automotive manufacturers are often risk averse when it comes to new, unproven technologies, and it is unlikely that AI will find first application in automotive manufacturing due to a number of factors, including return on investment, which is not clear and potentially involves a protracted period; lack of expertise in AI and limited resources to dedicate to this initiative; organisational and process challenges; and availability of non-AI based approaches with satisfactory results.

Automaker manufacturing executives are interested in technology opportunities that have strong, demonstrable pay-off potential, and this is especially true in the case of suppliers. A familiar concept for the industry that has reaped rich rewards over the years is automation and robotics. Ever since the first industrial robot, the Unimate, was installed in a GM factory in 1959, automation has been one of the driving forces for the exponential growth in production and efficiency of the automotive industry. Now with hundreds of robots busy assembling parts on the manufacturing lines, a new type of robot is making waves behind the scenes to prepare for the next automotive industry revolution.

The so called softbots, or digital workforces are programmed software that can help automate many processes that are rules-driven, repetitive and involve overlapping systems. With success in HR, IT and finance, the softbots can work 24/7 on otherwise boring, repetitive manual work that normally would take days for the human workforce to complete. This could result in a significant cost reduction along with a tremendous increase in efficiency. The automotive sector, among other industries, will significantly benefit from robotic process automation (RPA) by transforming various consumer and business applications.

AI adoption in supply chains is taking off as companies realise the potential it could bring to solve their global logistic complexities, and it has a particularly significant role to play in the automotive industry

In addition to business support functions such as HR, IT, and finance, RPA can contribute to a number of areas in automotive manufacturing, including inventory management, production monitoring and balancing, paper document digitization, supplier orders and payment processing, data storage and management, and data analytics and forecasting.

RPA could take over some or most of these processes to reduce resource costs. More importantly, it can integrate with other existing technologies such as object character recognition (OCR), text mining, and nature language processing (NLP) to make more data available from the shop floor for advanced and predictive analytics. The applications can be then developed to detect or predict quality issues much faster and recommend corrective actions based on historical data and expert knowledge.

Beyond manufacturing, RPA is also making an impact in enhancing regulatory compliances such as GDPR or CCPA by helping car companies building systems to auto-process data requests by millions of users.

RPA is the next logical step and a starting point for most automotive companies. Even though RPA is rule-based and does not involve intelligence, it would help to initiate the change in mindset that is required for future AI adoption in automotive environments. In addition, RPA offers relatively quicker ROI by providing benefits in terms of cost reduction and error reduction soon after implementation.

Data-intensive manufacturing leading to data lakes, powerful computing and the availability of efficient algorithms has made it easier to integrate AI into automakers technology roadmaps. Applying AI to current manufacturing operations on a smaller scale does not require massive capital investment. Trainable data is readily available which can facilitate intensive testing and deep learning. Cloud and elastic computing have provided the opportunity to scale computing power as required. It might be beneficial to partner up with AI and ML experts from academic institutions as well as from within automaker product development teams to sustain the digital transformation journey.

Having a comprehensive AI strategy is vital to the success and competitiveness of automotive manufacturers, regardless of how far-fetched the use cases may seem to executives today.

About the authors: Anirudh Ramakrishna is Senior Consultant Industry 4.0 at umlaut; Stephen Xu and Timothy Thoppil are Managing Principals at umlaut

This article is taken from Automotive Worlds December 2019 Special report: how will artificial intelligence help run the automotive industry?,which is available now to download

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Artificial intelligence gets to work in the automotive industry - Automotive World

Will the machines take control? Not if we focus on developing the skills that AI cannot replicate

From Siri, the virtual assistant in Apple mobile devices, to self-driving cars, artificial intelligence (AI) is progressing rapidly, outperforming humans at some tasks. As with the majority of the changes happening globally, there will be positive and negative impacts as AI continues to shape the world we live in. Every single one of us will have to reckon with our ability to balance the human way of life and the transition to the AI cosmos.

According to a report by the technology research group IDC, spending on AI is expected to reach US$46 billion by 2020 with no signs of slowing down. AI is definitely on the rise in both business and life in general. The question is, will humans eventually lose control as machines become super-intelligent? Unforeseen consequences are likely whenever a new technology is introduced, and AI is no exception.

It is obvious that AI is a disruptive technology, revolutionising businesses and bringing new approaches to decision-making based on measurable outcomes. It can enhance efficiency and production volume, while cultivating new opportunities for revenue to flourish.

We have to face the fact that humans arent always the best at tedious and repetitive tasks, whereas machines dont get tired or complain. This is where AI is starting to play an important role: freeing humans from drudgery so that we can focus on interpersonal relations and more creative work.

Is it true that robots and AI will destroy jobs? That is something we hear quite often. Everyone has their own opinions about the pluses and minuses of the technology. However, if you think about it in a positive way, AI is actually encouraging evolution in the job market, as candidates come to realise they need to develop new types of skills in order to secure fulfilling work amid rapid technological advancements.

The truth is, people will still work, but they will work better with the assistance of AI. In other words, the unparalleled duo of human and machines coming together will soon turn into the new normal in the workforce. Already there are many routine white-collar tasks such as answering emails, data entry and related responsibilities that can be handled by intelligent assistants if businesses are prepared to recognise the potential.

Away from the office, we can see that more and more people are living in smart homes or equipping their residences with hardware and software that can reduce energy usage and provide better security, among other benefits. AI is also having a profound impact on healthcare, leading to improved diagnosis and treatment of many conditions, leading to healthier citizens and healthier economies.

The ability of technology to answer more questions, solve more problems and innovate in previously unimaginable ways goes beyond the capacity of the human brain for better or worse, depending on how one perceives this subject. The elevation of technology will allow individuals to focus on higher functions, with improved quality living standards.

Challenges will continue to come and go, but the biggest one will be for humans to find their place in this new world, by staking a claim to all the activities that call for their unique human abilities.

A study by PwC forecast that 7 million existing jobs will be replaced by AI in the UK from 2017 to 2037. However, 7.2 million new jobs could be created as well. Yes, many humans are wondering whether they will be part of the 7 million or part of the 7.2 million. Living with this uncertainty is a struggle for many given the transformative impact of AI on our society and the economic, political, legal and regulatory implications that need to be prepared for.

At its core, AI is about imitating human thought processes. Human beings essentially have to teach AI the how-to of practically everything, but AI cannot be taught how to be empathic, something only humans can do. It is one thing to allow machines to predict and help solve problems; it is another to purposely make them control the ways in which people will be made redundant.

Therefore, it is vital for us to be more sceptical of AI and recognise its shortcomings together with its potential. By focusing more on training people in soft skills, starting in school, we can help produce a greater number of employable humans who will be able to work alongside machines to deliver the best of both worlds.

Arinya Talerngsri is Chief Capability Officer and Managing Director at SEAC - Southeast Asias Lifelong Learning Center. She can be reached by email at arinya_t@seasiacenter.com or https://www.linkedin.com/in/arinya-talerngsri-53b81aa. Explore and experience our lifelong learning ecosystem today at https://www.yournextu.com

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The impact of artificial intelligence on humans - Bangkok Post

The Seattle Seahawks will now be utilizing Amazon Web Services in bringing artificial intelligence ... [+] and machine learning to their game preparation in hopes to make more efficient on the field decisions. (Photo by Lachlan Cunningham/Getty Images)

Amazon has deep roots in Seattle which are about to get deeper. The company announced it will be expanding its services within the NFL, after partnering with the Seattle Seahawks to provide the team with its cloud and machine learning/artificial intelligence offerings. In the comprehensive partnership, the company will move the vast majority of its infrastructure to AWS and will also provide wide-ranging services, including computing, storage and database, as well as analytics intended to drive game strategy decisions for the team.

Amazons NFL Next Gen Stats offering has been providing player tracking data throughout this season and the Seahawks will utilize their data, that tracks the position of the ball on the field as well as every player 10 times per second, to provide detailed information on each players impact on the field. With this new partnership, the Seahawks will be able to use its own player data internally to develop a custom analytics and proprietary statistics platform combining the Next Gen Stats offering.

In addition to making possible a deeper understanding of team performance through analytics, the team will also use Amazons ML Solutions Lab, the companys program around machine learning and predictive analytics. The Seahawks will implement new capabilities through the software that will provide deeper analytics and insights through video footage. Using the AWS ML services, the team will be able to combine footage from training camp through the postseason, including NFL Coaches Film that gives a birds-eye view of all 22 players on the field, with Amazons Rekognition service, to better identify and understand opponents defensive and offensive strategies.

As our official cloud provider, AWS will enable the Seahawks to become a data-driven organization that uses the power of technology to fuel future championships. We chose AWS because of their relentless focus on innovation, their broad array of machine learning services, and proven to experience in supporting large sports organizations and enterprises around the world at scale, said Chip Suttles, Vice President, Technology, Seattle Seahawks. AWSs breadth and depth of services will help us to extract and pinpoint every possible advantage from the vast amount of data we collect, delivering the actionable insights we need to positively impact our decision-making.

Another service the Seahawks will utilize is Amazons SageMaker platform that will help develop a fully managed service around the building, training and deploying of machine learning models. These models can analyze football statistical information such as quarterback hurries, knockdowns or sacks and are also able to judge how a quarterback handles pressure in the pocket or throws downfield when they are under heavy duress by a defense. These new capabilities will not only be able to provide insights to Seahawks coaches but will also be able to make data-driven recommendations to the team, once the system gets smarter through information being fed into its software.

The Seahawks previously used Microsofts services and its data environments were stored on Microsfts Azure cloud. Even with the Amazon tie-in, the team will continue to utilize Microsofts products and services through the teams partnership, as well as through Microsofts integration with the NFL.

Amazons relationship with the NFL has slowly but steadily expanded over time, with its streaming of Thursday Night Football for the past three seasons over Amazon Prime Video and the addition of games on Amazons Twitch platform. Amazon has aimed to enhance the fan experience through its Next Gen Stats offering and has used Seahawks quarterback Russell Wilson in its advertisements for the product. Amazon CEO Jeff Bezos was also reportedly endorsed by NFL owners to become an owner within the league in the near future.

Partnerships like this will become much more common going forward as data has proven to be the next frontier into the analysis of sports, not only from a game play perspective but also from a business perspective. Understanding how to get an edge on the field or utilizing a potential revenue stream makes these types of offerings very enticing and something each team will likely be engaging in the very near future.

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Seattle Seahawks Select Amazon In Utilizing Artificial Intelligence To Help Make Smarter Decisions On The Field - Forbes

November 30, 2019 Fujifilm Medical Systems U.S.A. is showcasing REiLI, the company's global medical imaging and informatics artificial intelligence (AI) technology initiative at the 2019 Radiological Society of North America's (RSNA) annual meeting.

"At RSNA 2019, we look forward to sharing the AI insights and advances we've made by working closely with clinical and research partners for several years," said Takuya Shimomura, chief technology officer and executive director, Fujifilm. "Ultimately, the long-term goal of our AI initiative is to help providers make better decisions that improve patient lives."

Under the REiLI brand, Fujifilm is developing AI technologies that strongly support diagnostic imaging workflow, leveraging the combination of its deep learning innovations and distinct image processing heritage. Applications currently in development include, but are not limited to: Region Recognition, an AI technology that helps to accurately recognize and consistently extract organ regions, regardless of deviations in shape, presence or absence of disease, and imaging conditions; Computer Aided Detection, an AI technology to reduce the time of image interpretation and support radiologists' clinical decision making; Workflow Support, using AI technology to realize optimal study prioritization, alert communications of AI findings, and report population automation.

"Our latest Synapse 7x brings diagnostic radiology, mammography and cardiology together on the server-side, enabling immediate interaction with these modality imaging data sets through a single AI-enabled platform," said Bill Lacy, vice president, medical informatics, Fujifilm. "We're excited to debut this solution for our U.S. customers at RSNA 2019, showing our commitment to progressing AI technology to empower physicians to make more efficient and impactful care decisions."

RSNA attendees are encouraged to learn more about REiLI at Booth #4111 and participate in the following Fujifilm-hosted activities.

At booth #4111, attendees can visit Fujifilm's AI Lab. The lab will feature dedicated workstations demonstrating REiLI use cases within Synapse PACS. Attendees can witness first-hand the speed and depth of the integrated workflows achieved by unifying Fujifilm's REiLI technology with the company's server-side PACS system. Featured in the AI lab will be Fujifilm developed algorithms, to include CT lung nodule, intracerebral hemorrhage, cerebral infarction MR and CT, spine label and bone temporal subtraction to name a few. In addition to the Fujifilm AI development, the AI lab will showcase its strengths by supporting a multitude of integration points in support of partner vendor and provider developed algorithms. This will include Riverain's lung nodule, MaxQ's stroke, Lunit's Chest and 2-D Mammography, LPixel's MR Aneurysm, Koios' US breast, Aidoc's pulmonary embolism and Gleamer's bone fracture.

For more inform rsna.fujimed.com

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Fujifilm Showcases Artificial Intelligence Initiative And Advances at RSNA 2019 - Imaging Technology News

Discussions, articles, and reports about the AI opportunity across the financial services industry continue to proliferate amid considerable hype around the technology, and for good reason: The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, per Autonomous Next research seen by Business Insider Intelligence.

Most banks (80%) are highly aware of the potential benefits presented by AI, per an OpenText survey of financial services professionals. In fact, many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they're currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Business Insider Intelligence. Certain AI use cases have already gained prominence across banks' operations, with chatbots in the front office and anti-payments fraud in the middle office the most mature.

The companies mentioned in this report are: Capital One, Citi, HSBC, JPMorgan Chase, Personetics, Quantexa, and U.S. Bank

Here are some of the key takeaways from the report:

In full, the report:

Interested in getting the full report? Here are two ways to access it:

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AI IN BANKING: Artificial intelligence could be a near $450 billion opportunity for banks - here are the strat - Business Insider India

Featured SpeakersJed DoughertyGlobal VP of Field Engineering, DataikuRegister

Machine Learning and AI were hot in 2019, but whats next for AI in 2020? The software side of working with AI improved a lot this year. But the hardware infrastructure side is still pretty complex, and for those who want to take advantage of GPU technology, that goes double. The truth is that AI hardware, both for fast training and effective inferencing, can be expensive, and its obsolescence cycles are quick. Thats a blocker.

But the cloud, container technology and smart software to orchestrate it all can help. Intelligent auto-scaling can help as well. Economically efficient management of specialized hardware and multi-cloud container computing strategies are the next frontier in AI. Theyre also key to AIs continued journey to the mainstream.

To learn more, join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust with Jed Dougherty, Global VP of Field Engineering at Dataiku, an enterprise AI and machine learning platform.

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Artificial Intelligence in 2020: The Architecture and the Infrastructure - Gigaom

Whoever controls the strongest artificial intelligences controls the world.

Artificial intelligence is the most important technology of the 21st century. It is therefore important to understand global ambitions and movements.

In this article I examine the global artificial intelligence industry and in this context consider the aspects of politics, data, economy, start-ups, financing, research and infrastructure.

I will only briefly discuss the current superpowers China and the USA, as I will dedicate a separate article to each of them.

The question that we must ask ourselves in the end is how humanity will deal with the global challenges.

So far, the first wave of digitization has developed without much government influence. Although there are now plans to break Google's monopoly (USA and Europe), for example by imposing European fines on Google and Facebook, politics is lagging behind the market by over a decade.

As far as AI is concerned, for the first time in recent history I have observed a multitude of initiatives, strategies and actions by dozens of governments around the world - with very different goals and approaches.

Artificial intelligence is and remains an issue that politicians and administrations of all nations have to deal with.

AIs are relevant for climate protection and economic policy.

AIs influence the governance of domestic industry, the security and privacy of citizens.

A long-term strategy for the establishment and development of own AIs is crucial. But it is also expensive. Europe in particular has problems deciding in favour of long-term and investment-intensive strategies.

Fabian Westerheide

China has a clear vision of how country wants to master artificial intelligence. From China's point of view, artificial intelligence is an important tool for strong foreign policy, military dominance, economic success and for controlling one's own population.

The USA benefits from a strong research cluster and the super corporations Google, Microsoft, Facebook and Amazon, each of which is in the lead of the AI development.

Although the USA has not yet found a red line under President Trump, the state has been promoting the research and implementation of AIs for decades through its countless secret services and ministries.

Canada and Israel have become equally important but smaller players in the global competition for AI rule.

Israel, always very technologically strong, has more AI companies than Germany and France put together (see also our study Global Artificial Intelligence Landscape). In Israel, there is a close network of universities, access to the Asian and American capital markets, close cooperation with the military and the government. The Israeli company Mobileye was bought by Intel for $15 billion and is just one example of a thriving AI ecosystem.

Canada benefits greatly from the renaissance of deep learning in the last 7 years. Geoffrey Hinton, Yann LeCun and Yoshua Bengio are three of the strongest researchers in this technology. All three have researched at different times in the Canadian Institute for Advanced Research. Together they have survived the last "AI winter" and have been shaping the market ever since.

In addition, Canada has a clear AI strategy, research, investment and implementation have been promoted for years.

Also worth mentioning are Japan, Korea and India, which have good prerequisites for playing a relevant role in the AI industry in the coming years.

A reading reference at this point is the report of national strategies of artificial intelligence of the Konrad Adenauer Foundation (Part 1 and Part 2).

While politics provides the framework conditions for research, financing, education, data, promotion and regulation, in the medium term AIs must be developed by companies and brought onto the market.

First of all, national interests have to be taken into account.

These include, often with their own agenda and independently, global corporations with their own AI research and AI products.

In my view, Google (Alphabet), Amazon and Microsoft are global leaders. The Chinese Internet giants Alibaba, Baidu and Tencent are also relevant players.

There are two types of companies: Those that develop and sell AI as a core product and those that use AI to complement their value chain.

Either way, any company active today has to deal with artificial intelligence. On the one hand, AIs can replace existing business models, and on the other hand, they can be integrated into countless company-internal processes: Accounting, controlling, production, marketing, sales, administration, personnel management and recruiting.

By the way, this is the primary driver of applied artificial intelligence: reduce costs and maximize profits.

And, of course, it's also about control. Every AI used takes over activities that were previously performed by humans. Often, after a while of training, the AI is faster, more efficient and cheaper than the human being was before.

People become ill, they need holidays, food and sleep. They have to be entertained, quit or retire. AIs work 24/7 and do not demand a wage increase.

The more companies use AIs, the more independent they become of human labour.

The foundation of any artificial intelligence is data. We therefore need data on several points.

First of all, we need data for the research and training of narrow artificial intelligences. The more digital your business model is, the more data you have.

For this reason, marketing leaders (Google, Facebook), software companies (Salesforce, Microsoft) and e-commerce retailers (Zalando, Amazon) have been heavily involved in AI for years.

Some banks also recognized the trend early on. Therefore Goldman Sachs and J.P. Morgan have already recruited thousands of employees with a focus on machine learning and data science.

Those who have their own data can achieve an enormous competitive advantage.

Those who have no data have to collect, store and evaluate data.

However, this is where the different national data protection laws come in, which is why Europe is at a disadvantage.

GDPR/DSVGO may indeed have the good intention to create a European data internal market, but currently form an enormous location disadvantage for Europe.

The fear of the regulation paralyzes whole industries. Personal discussions with clinics and doctors showed me that the health industry no longer shares any data. This literally costs human lives, because this obstacle is detrimental to health research and life-prolonging algorithms.

This is just one example among many.

Uncertainty about data is paralysing our entire European industry. For fear of penalties, data is not collected at all. We are creating a culture of data anxiety at a time when data is actually our strength.

Europe is the most important data market in the world, but we are wasting our potential.

China, on the other hand, is the extreme opposite. The state helps with a lively exchange and centralization of data (more on this in the chapter on China). In addition, the population has fewer concerns about the free handling of data.

De facto, privacy no longer exists in the 21st century. Every digital action is measured and stored. However, we Europeans are sticking to an old ideal.

Start-ups are essential for any economy because they take on two essential functions of an ecosystem.

Start-ups are drivers of innovation. These young companies are often more courageous, faster and more flexible in developing new products than established companies. Backed by the capital of venture capital funds and business angels, start-ups take high risks in the expectation of extraordinary success.

Although 95% of start-ups do not survive the first 5 years, the entire ecosystem benefits from them.

Companies can buy new products and innovations through acquisitions.

Former employees find new jobs and transfer their knowledge.

Investors and founders learn and take their knowledge with them into new projects.

Perhaps the young company will survive the 5-year threshold. It secures financing (from seed to IPO), gains talent, grows, develops products for which customers pay, scales and becomes a corporation. Facebook, Google, Apple, Amazon, Uber - all started out as start-ups and are now dominant market leaders.

Charles-douard Boue, former CEO of Roland Berger, said at the 2018 Rise of AI conference that the next wave of trillion-dollar companies will mainly be AI companies.

This won't work without start-ups. That's why we need to encourage building start-ups.

The rediscovery of Deep Learning was only the beginning. The field has evolved through new approaches from CNN, GAN to evolutionary algorithms (Prof. Damian Borth's presentation at the 2017 Rise of AI conference is a good introduction to deep learning).

Computational linguistics around NLP and NLG has also made enormous leaps.

Today, hundreds of thousands of narrow artificial intelligence applications are based on the research results of the last 30 years, after we reached the critical volume of computing power and data availability in 2012.

Where do the research results come from?

On the one hand, they come from universities. MIT, Stanford, Carnegie Mellon University and Berkley are lighthouses in AI research (see also the AI index from Stanford).

MIT alone is investing 1 billion dollars in the training of new AI degree programmes by 2020.

On the other hand, companies have now become a major driver of AI research. You should know Google DeepMind. Microsoft has over 8,000 AI researchers.

Leading minds conduct research for corporations with more data and financial resources: Richard Socher (Salesforce), Yann LeCun (Facebook), Andrew Ng (until 2017 Baidu) or Demis Hassabis (Google).

European universities and corporations, on the other hand, are not leaders in the field of AI research. Of course, we also have smart minds like Prof. Jrgen Schmidhuber, Prof. Francesca Rossi and Prof. Hans Uszkoreit.

In addition, there are AI courses at KIT, TU Munich, TU Berlin, the University of Osnabrck (Cognitive Science), Oxford and Cambridge University.

But all this is just mediocrity and not internationally recognized top-level research.

Instead, the DFKI (German Research Institute for Artificial Intelligence), dozens of Max Planck Institutes and Fraunhofer Institutes in Germany in particular are primarily engaged in applied research. But even these institutes do not manage to play in the first league in the global competition for talent, data and capital.

But it is precisely research that will be decisive in the coming decades when it comes to the question of who will develop the first general artificial intelligences.

Video recommendation: Lecture by Prof. Hans Uszkoreit at the Rise-of-AI Conference 2017 on Super Intelligence.

By infrastructure I mean not only the availability of data but also the necessary computing and performance capacities.

NVIDIA used to be known for their graphics cards among gamers. Today, NVIDIA is one of the leading manufacturers of GPUs, which are increasingly used for AI applications. Google, Intel and many other companies are very active in the development of new AI chips in various forms.

At the same time, Microsoft, AWS, Google and IBM are expanding cloud capacity around the world to meet growing demand.

While China will focus strongly on 5G, which is critical for real-time AI applications and the networked industry, Europe will not play a leading role in this technology issue either.

The development of artificial intelligence is expensive.

Top AI researchers are rare and receive salaries of up to 300,000 per year.

Data must be collected, sorted and labelled. Developing AI models takes time for experiments, mistakes and new methods.

AIs need data, must be trained and educated.

These costs are borne by companies, start-ups, investors and also the state.

China has understood this and is investing over 130 billion euros in the Chinese AI market. Provinces such as Beijing, Shanghai and Tianjing are each investing tens of billions in local AI industry.

In the USA, Google, IBM, Microsoft, Amazon, Facebook and Apple have already invested over 55 billion dollars internally by 2015.

Without money, there is no artificial intelligence.

And once again Europe is too stingy to invest in the future.

A comparison of the orders of magnitude: In 2018, the German Bundestag had budgeted as much as 500,000 for AI funding. A further 500 million is planned, but the funds are not yet available.

Progress will not succeed in this way.

At the same time, China is financing 400 new chairs for AI. To date, we have seen nothing of the 100 new professorships planned under the German AI Strategy.

In this context, I would like to praise Great Britain because it is going against the trend in Europe - despite Brexit. More money is being made available on the island for start-ups and universities in the field of artificial intelligence.

If you want to know more about the current state of AI, I recommend the State-of-AI-Report 2019 and my presentation of the Rise of AI 2019 as video.

As I mentioned earlier, Europe is currently losing the competition for the leading AI nations.

While Europe is still considering whether to compete at all, China, the US, Israel, the UK and Canada are already competing for data, markets and talent.

Our problems in Europe are homemade, they are the result of our inertia, lack of vision and ambitions.

There is a lack of money for education. Not only are our schools and universities underfunded, but so is the education labour market. Our children are not learning enough about digital skills. Our students rarely take AI-relevant subjects. Our working population lacks retraining opportunities that also meet the needs of the growing digital industry.

The transfer of research results to industry is sluggish. Results either disappear into the drawer, or the IP transfer is in bureaucratic terms a horror, especially for young companies and spin-offs.

Our European AI start-ups are significantly underfinanced. Those who currently need money from investors must market e-bikes and e-scooters, but they should not include technology. The more complex the product, the more difficult it is to get capital. The simpler the business model, the faster the accounts are filled.

Although many talents from Asia and America want to work in Europe, it has become bureaucratically complicated. Since the wave of refugees, the offices have been overwhelmed. It is almost impossible to hire talented AI developers from Iran, Russia or China. There is currently a spirit of rejection rather than openness in Europe.

Europe lacks a single strategy. Countries such as Finland, Sweden, the Netherlands or France have their own AI strategies and, moreover, a great deal of ambition. Germany, in particular, is blocking a common European approach and thus possible success.

When I was with the European Commission in 2018, a Bulgarian researcher said that she would be happy if her country had a plan at all. According to her, entire sections of Europe are significantly worse off than we are in Western Europe.

I am not saying that politics must solve all our problems. Companies still have to build products, founders have to start start-ups, VCs have to finance these start-ups and researchers have to do research.

But politicians can support us with a clear strategy. It can build up regulatory structures instead of inhibiting them. It can create incentives for investment and act as a role model. And it must be a matter of course for politicians to take care of the education of pupils, students and qualified further education in general.

On paper you can read all this (AI strategy of the German Federal Government), but in practice nothing happens.

Europe is marked by power struggles, egoism and technology phobia.

But Europe is only part of the world and must adapt to a global power order.

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The Artificial Intelligence Industry and Global Challenges - Forbes

Thanksgiving dinners are known to be the stage of controversial discussions: religion and politics are amongst the conversation topics that make these family gatherings awkward for some...and dreadful for many.

So, for this decades last Thanksgiving, how about switching it up and talking about Artificial Intelligence (AI)?! After all, every company seems to be doing AI. You can do your part to help explain it.

Here are some simple, many even silly, steps to get your Thanksgiving meal back on track with AI.

What the heck is AI anyways?!

If its a 5-year-old or a 75-year-old that asks today: What is AI?, use the following three steps:

1) The academic explanation

You could say: "Artificial Intelligence refers to the science that helps computers do things that only humans typically can do. For instance: making a decision as the result of something we learned over time, or, altering our opinion based on new information, deducting the answer to a complex situation based on incomplete data.

If this intro works, then you can further theorize how humans have special powers like imagination, judgment or deduction.

OR, you can move to step #2.

2) Pull up a calculator

Many of my fellow technologists will probably cringe at the idea that one could reduce the concept of AI to a calculator. But they are suffering from the Curse of Knowledge: they know more than most people do and they forget what it feels to not know.

To understand AI and the service it provides humans, youve got to start with the most basic concept attached to AI: the algorithmic sequence. AI is the result of algorithms and their sequence. If your audience doesnt understand that, you wont get very far.

Now, ask your audience to grab a pen and a paper. Give your human subject a series of complex calculations. Time them. Then, enter the same sequence into the calculator while you ask the human to time you as you're getting the answer. If all goes well, the human will witness that the machine was much faster. They should also understand that a) the machine stores more information than their brain ever could, and that b) it can retrieve the right answer 100% of the time, and faster than they could ever hope. You can probably also explain that the machine never will fail as a result of stress or confusion or emotions that only humans have.

Now youre ready for step #3.

3) The "Calculator 2.0 Moment": Play Twenty Questions

Twenty questions is a simple game that requires deductive reasoning and creativity. One player secretly thinks of a thing (typically an animal, vegetable, or mineral). The other players try to guess their secret by asking 20 questions.

Spend 5 or 10 mins playing Twenty Questions with your little nephew or grandma. Spend enough time playing the game so they can understand what deductive reasoning and creativity feel like and curse of knowledge.

Now, pull up an Amazon Alexa (or similar smart device). Play Twenty Questions with it. This should result in what I call the Calculator 2.0 Moment. Its that moment when humans realize that machines can do things they can.

Its that moment when they realize that a big part of "our lives run on math.

And when things run on math, they can be decoded, recoded and improved to provide better results, faster.

Thats what Artificial Intelligence is all about.

Happy Thanksgiving to all!

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Need a New Topic for Thanksgiving Dinner? How to Explain Artificial Intelligence (AI) to Anyone...and Make it Fun! - Forbes

AI talk: FX Week Europe panellists dont see much use for complex machine learning in FX

Artificial intelligence can be particularly useful in asset classes where there are thousands of instruments available to trade, but it is not deemed as practical in a market such as foreign exchange, where the overall number of currency pairs is limited and even less so in the majors, remarked panellists at the 2019 FX Week Europe conference.

While the panellists did not completely disregard the potential for AI in FX, they did not believe it is as relevant as it is for equities, for example.

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Artificial intelligence in FX 'may be hype' - FX Week

Automotive technology concept.

How are data and AI transforming transportation?originally appeared onQuora:the place to gain and share knowledge, empowering people to learn from others and better understand the world.

AnswerbyJonathan Matus, CEO and Founder,Zendrive, onQuora:

While our growing dependencies on mobile phones stand to threaten road safety and increase rates of distracted driving, other technology innovations can work in safetys favor. Developments in 5G networks, autonomous vehicles, and artificial intelligence are poised to transform the way we drive and the safety of our roads.

5G will have a positive impact on road maintenance with faster data collection creating new possibilities around automation. Today, road crews have to physically go on-site to inspect a problem and determine what next steps are required. But through new video and sensor data, road maintenance crews will receive alerts of life-threatening hazards faster than ever. Connected vehicles equipped with dash cams will generate crowdsourced footage of potential debris and other hazards so that crews can act fast to alert drivers in the area and find safe solutions. Sensors on smartphones can produce similar insights already and offer insights in the interim. In addition to this, departments will be able to rank the urgency of various jobs by analyzing data from each location.

According to a report,94% of vehicle accidentsin the US involve human error and are potentially avoidable. With autonomous vehicle technology especially, theres the potential to essentially eliminate human error from the risk equation, decreasing the number of collisions and improving overall road safety. To achieve full autonomy, the onboard computers on self-driving cars need to make use of cameras and radar sensors to generate a 3D view of the vehicles surroundings. One of the challenges to this lies in getting the information needed to make split-second decisions in real-time. Eventually, 5G and artificial intelligence will be leveraged in tandem to give these vehicles a more accurate view of the road, making cars more functional and safe.

Artificial intelligence is also ushering in a new chapter for smartphones. Even though most of us dont realize it, artificial intelligence is powering many of the features on several mobile apps today. These include Map apps, as well as virtual assistants like Google Assistant, Cortana, and Siri. With mobile apps running telematics in the background, drivers gain access to the latest technologies in driver safety, artificial intelligence, and 5G in a single device. Drivers are also able to use voice commands to look for gas stations, perform internet searches, and communicate with friends and family instead of physically using their phones while driving. Even more, artificial intelligence paired with telematics gives drivers access to real-time information on fuel usage, vehicle location, driver behavior, and speed.

This questionoriginally appeared onQuora- the place to gain and share knowledge, empowering people to learn from others and better understand the world. You can follow Quora onTwitterandFacebook. More questions:

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The Surprising Way Artificial Intelligence Is Transforming Transportation - Forbes