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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

December 1, 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

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Yes, artificial intelligence (AI) is proving itself to be a worthwhile tool in the business arena at least in focused, preliminary projects. Intelligent chatbots are a classic example. Now its a question of how quickly it can be expanded to deliver on a wider basis across the business to automate decisions around inventory or investments, for example.

Theres progress on this front, as shown in McKinseys latest survey of 2,360 executives, which shows a nearly 25 percent year-over-year increase in the use of AI in various business processes and there has been a sizable jump in companies spreading AI across multiple processes.

A majority of executives in companies that have adopted AI report that it has increased revenues in areas where it is used, and 44 percent say it has reduced costs, the surveys authors, Arif Cam, Michael Chui, and Bryce Hall, all with McKinsey, state.

The results also show that a small share of companies the authors call them AI high performers are attaining outsize business results from AI. Close to two in three companies, 63 percent, report revenue increases from AI adoption in the business units. Respondents from high performers are nearly three times likelier than their lagging counterparts to report revenue gains of more than 10 percent, the survey shows.

The leading AI use cases include marketing and sales, product and service development, and supply-chain management. In marketing and sales, respondents most often report revenue increases from AI use in pricing, prediction of likelihood to buy, and customer-service analytics, the surveys authors report. In product and service development, revenue-producing use cases include the creation of new AI-based products and new AI-based enhancements. And in supply-chain management, respondents often cite sales and demand forecasting and spend analytics as use cases that generate revenue.

What are these high performers doing differently? Strategy is a key area. For example, 72 percent of respondents from AI high performers say their companies AI strategy aligns with their corporate strategy, compared with 29 percent of respondents from other companies. Similarly, 65 percent from the high performers report having a clear data strategy that supports and enables AI, compared with 20 percent from other companies. Also, the application of standardized tools to be used across the enterprise is more likely to be seen at high performers.

Adoption of Strategic AI Approaches:

Retraining workers is also a key differentiator, the survey shows. One-third of high performers, 33%, indicate the majority of their workforce has received AI-related training over the past year, compared to five percent of lagging organizations. Over the next three years, 42% of high performers intend to extend such training to most of their workers, versus only 17% of their lagging counterparts.

For AI to take hold, the McKinsey authors urge ramping up workforce retraining. Even the AI high performers have work to do in several key areas, the surveys authors point out. Only 36 percent of respondents from these companies say their frontline employees use AI insights in real time for daily decision making. A minority, 42 percent, report they systematically track a comprehensive set of well-defined key performance indicators for AI. Likewise, only 35 percent of respondents from AI high performers report having an active continuous learning program on AI for employees.

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It Pays To Break Artificial Intelligence Out Of The Lab, Study Confirms - 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

Artificial intelligence (AI) -- the capability of a machine to mimic human thinking and behavior -- is one of the biggest growth trends today.Spending on AI systems will increase by more than two and a half times between 2019 and 2023, from $37.5 billion to $97.9 billion, for a compound annual growth rate of 28.4%,according to estimates by research firm IDC. Other sources are projecting even more torrid growth rates.

There are two broad ways you can get exposure to the AI space:

With this background in mind, let's look at which AI stocks are performing the best so far this year (through Nov. 25) and which one is my choice for best AI stock for 2020.

Image source: Getty Images.

The following chart isn't meant to be all-inclusive, as that would be impossible, and the chart has limits on the number of metrics. Notable among the companies missing areAdvanced Micro Devices and Intel. They were left out largely because NVIDIA is currently the leader in supplying AI chips. While there are things to like about shares of both of these companies, NVIDIA stock is the better play on AI, in my view.

Data by YCharts.

Graphics processing unit (GPU) specialist NVIDIA (NASDAQ:NVDA), e-commerce and cloud computing service titanAmazon, computer software and cloud computer service giant Microsoft, Google parent and cloud computing service provider Alphabet, old technology guard and multifaceted AI player IBM, and Micron Technology, which makes computer memory chips and related storage products, would best be put in the first category above. They produce and sell AI-related products and/or services. They're all also probably using AI internally, with Amazon and Alphabet being notably heavy users of the tech to improve their products.

iPhone makerApple (NASDAQ:AAPL), social media leader Facebook (NASDAQ:FB), video-streaming king Netflix, and Stitch Fix, an online personal styling service provider, would best be categorized in the second group since they're either primarily or solely using AI to improve their products and services.

Now let's look at some basic stats for the three best performers of this group.

Company

Market Cap

P/E(Forward)

Wall Street's 5-Year Estimated Average Annual EPS Growth

5-Year Stock Return

Apple

NVIDIA

Facebook

S&P 500

--

--

Data sources: YCharts (returns) and Yahoo! Finance (all else). P/E = price-to-earnings ratio. EPS = earnings per share. Data as of Nov. 25, 2019.

On a valuation basis alone, Facebook stock looks the most compelling when we take earnings growth estimates into account. Then would come Apple and then NVIDIA. However, there are other factors to consider, with the biggie being that projected earnings growth is just that, projected.

There's a good argument to be made that NVIDIA has a great shot at exceeding analysts' earnings estimates. Why? Because it has a fantastic record of doing so, and all one needs to do is listen to enough quarterly earnings calls with Wall Street analysts to realize why this is so: A fair number of them don't seem to have a strong grasp of the company's operations and products. (I'm not knocking, as most analysts don't have technical backgrounds, and they cover a lot of companies.)

Facebook stock probably has the potential to continue to be a long-term winner. But it's relatively high regulatory risk profile makes it not a good fit for all investors. Moreover, it will likely have to keep spending a ton of money to help prevent "bad actors" from using its site for various nefarious purposes. Indeed, this is one of the major internal functions for which the company is using AI. It also uses the tech to recognize and tag uploaded images, among other things.

Apple uses AI internally in various ways, with the most consumer-facing one being powering its voice assistant Siri. It's the best of these three stocks for more conservative investors, as it has a great long-term track record and pays a modest dividend.NVIDIA, however, is probably the better choice for growth-oriented investors who are comfortable with a moderate risk level.

Image source: Getty Images.

NVIDIA is the leading supplier of graphics cards for computing gaming, with AMD a relatively distant second. In the last several years, it's transformed itself into a major AI player, or more specifically, a force to be reckoned with in the fast-growing deep-learning category of AI. Its GPUs are the gold standard for AI training in data centers, and it's now making inroads into AI inferencing. (Inferencing involves a machine or device applying what it's learned in its training to new data. It can be done in data centers or "at the edge" -- meaning at the location of the machine or device that's collecting the data.)

NVIDIA is in the relatively early stages of profiting from many gigantic growth trends, including AI, esports, driverless vehicles, virtual reality (VR), smart cities, drones, and more. (There is some overlap in these categories, as AI is involved to some degree in most of NVIDIA's products.) There are no pure plays on AI, to my knowledge, but NVIDIA would probably come the closest.

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The Best Artificial Intelligence Stocks of 2019 -- and The Top AI Stock for 2020 - The Motley Fool