Daily Archives: February 27, 2020

The medical technology conference med.con 2020, hosted by ENGEL at its Technologieforum in Stuttgart during mid February, was a huge success. The conference was a full house as more than 100 delegates attended it.

The main talk point at the conference was patient safety through advanced technology and was discussed from the various perspectives of plastics processing in the cleanroom and conveyed in a tangible way using live machine exhibits.

With artificial intelligence and big data being the focus, it was deliberated that the potential for more quality, safety, and cost-efficiency in medical technology is yet to be fully exploited.

Summing up the massive challenge that the volume of data generated is increasing, but the use of the data is not, Uwe Herbert, IT manager at Ypsomed, a manufacturer of injection systems for self-medication, in his keynote address mentions, We are passing up opportunities here

Uwe advocates that it is necessary to link the IT system of the individual department in the company and provide the employees with the freedom they need to experiment with the new possibilities to improve the quality of the products and reduce the unit costs. However, according to Uwe, the complexity of these projects is often underestimated.

Speaking of artificial intelligence, Christian Pommereau, principal engineer with pharmaceutical company Sanofi-Aventis Deutschland, emphasizes, We need to shift up a gear when it comes to artificial intelligence. To avoid the plastics processing industry losing touch, we need everyone around the table, adds Christian. He has witnessed within his own group of companies how far ahead the drug production industry is in this field.

Both the above-mentioned speakers sparked a lively discussion. It became clear that the industry has long recognized the great potential that Industry 4.0 has to offer. But obstacles often remain to adopt the new technologies for reflecting the specific requirements of cleanroom production. For instance, the validation of dynamic process control with the help of intelligent assistance, an important feature of the smart factory, has to be planned in detail and designed safely.

Christoph Lhota, vice president, ENGEL medical, reported on how ENGELs iQ weight control assistance system can be integrated into rules and regulations accepted by the auditors, based on ongoing development work.

The ENGEL developers have investigated various approaches to the validation process and ultimately derived a procedure that defines process windows for the parameters to be retroactively adjusted, enabling the validation of dynamically controlled processes in conformity with both EN ISO and the FDA.

In his keynote, Christoph gave an outlook on other topics that are gaining in importance in medical technology and on which ENGELs developers are working intensively. These comprise of injection molding of liquid silicone rubber in the cleanroom, efficient injection molding of very small batch sizes and sterile injection molding, like cleanroom class ISO 5 is increasingly required in plastics processing.

Talking about ISO 5, Christoph informs, It is a totally different planet. The opening speed of the injection molding machine is significant here. To specifically adapt its machines, robots and technologies to this new class of requirements, ENGEL operates its own clean room at its headquarters in Schwertberg.

Among the eight presentations during the keynote session, other speakers were Martin Maier from Waldorf Technik, Reinhard Steger from Braunform, Martin Jungbluth from Max Petek Reinraumtechnik, and Jrg Leonhartsberger and Claus Wilde from ENGEL.

During the conference, especially in the breaks, and following the talks, ENGEL opened up its technology center with live exhibits and a partner exhibition.

ENGELs high level of expertise in systems solutions was noticeable in the clean room injection molding applications. Sophisticated medical products were manufactured in highly-integrated and automated production cells throughout the event.

There were thick-walled housing parts which can be produced in an 8-cavity mold using servo-electric Vario-Spinstack technology from Hack Formenbau in particularly short cycle times and with a correspondingly low unit cost, thanks to the two-component process.

It also showcased needle holders for 1 ml safety syringes in a 16-cavity mold by Fostag Formenbau with a particularly low shot weight of 0.08 grams per part. The needle holders very thin and different wall thicknesses require extremely precise process control, which ENGEL ensures with iQ weight control.

The needle holders are taken off by a viper linear robot and transferred to the pipe distribution system, developed by ENGEL and made completely of stainless steel, in order to package the filigree mouded parts sorted by cavity.

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Artificial Intelligence and Big Data forms focus on ENGEL's medical technology conference - Plastics Insight

The time an airplane spends waiting for a gate after landing or waiting in line to take off could also be reduced. A group at SITA focused on airport management systems is helping to design technology that can synthesize data from many sources, including changing aircraft arrival times, weather conditions at destination airports and logistical issues to improve runway schedules and gate assignments.

Artificial intelligence software can also make a difference with rebooking algorithms, Mr. Etzioni said. When weather or mechanical issues disrupt travel, the airlines speed in recomputing, rerouting and rescheduling matters, he said.

The data streams get even more complex when the whole airport is considered, Ms. Stein of SITA said. A number of airports are creating a digital twin of their operations using central locations with banks of screens that show the systems, people and objects at the airport, including airplane locations and gate activity, line lengths at security checkpoints, and the heating, cooling and electrical systems monitored by employees who can send help when needed. These digital systems can also be used to help with emergency planning.

The same types of thermal, audio and visual sensors that can be used to supply data to digital twins are also being used to reduce equipment breakdowns. Karen Panetta, the dean of graduate engineering at Tufts University and a fellow at the Institute of Electrical and Electronics Engineers, said hand-held thermal imagers used before takeoff and after landing can alert maintenance crews if an area inside the airplanes engine or electrical system is hotter than normal, a sign something may be amiss. The alert would help the crew schedule maintenance right away, rather than be forced to take the aircraft out of service at an unexpected time and inconvenience passengers.

At the moment, people, rather than technology, evaluate most of the data collected, Dr. Panetta said. But eventually, with enough data accumulated and shared, more A.I. systems could be built and trained to analyze the data and recommend actions faster and more cost effectively, she said.

Air travel isnt the only segment of the transportation industry to begin using artificial intelligence and machine learning systems to reduce equipment failure. In the maritime industry, a Seattle company, ioCurrents, digitally monitors shipping vessel engines, generators, gauges, winches and a variety of other mechanical systems onboard. Their data is transmitted in real time to a cloud-based A.I. analytics platform, which flags potential mechanical issues for workers on the ship and on land.

A.I. systems like these and others will continue to grow in importance as passenger volume increases, Ms. Stein said. Airports can only scale so much, build so much and hire so many people.

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Air Travelers Cant See All of It, but More Tech Is Moving Them Along - The New York Times

The White Houses recent efforts to chart a national artificial intelligence (AI) policy are welcome and, frankly, overdue. Funding for AI research and updating agency IT systems is a good start. So is guidance for agencies as they begin to regulate industry use of AI. But theres a glaring gap: The White House has been silent about the rules that apply when agencies use AI to perform critical governance tasks.

This matters because, of all the ways AI is transforming our world, some of the most worrying come at the intersection of AI and the awesome power of the state. AI drives the facial recognition police use to surveil citizens. It enables the autonomous weapons changing warfare. And it powers the tools judges use to make life-changing bail, sentencing and parole decisions. Concerns about each have fueled debate and, as to facial recognition in particular, new laws banning use.

Sitting just beyond the headlines, however, is a little-known fact: AI use already is pervasive in government. Prohibition for most uses is not an option, or at least not a wise one. Needed instead is a frank conversation about how to give the government the resources it needs to develop high-quality and fairly deployed AI tools and build sensible accountability mechanisms around their use.

We know because we led a team of lawyers and computer scientists at Stanford and New York universities to advise federal agencies on how to develop and oversee their new algorithmic toolkit.

Our research shows that AI use spans government. By our estimates, half of major federal agencies have experimented with AI. Among the 160 AI uses we found, some such as facial recognition are fueling public outcries. But many others fly under the radar. The Securities and Exchange Commission (SEC) uses AI to flag insider trading; the Centers for Medicare and Medicaid Services uses it to ferret out health care fraud. The Social Security Administration is piloting AI tools to help decide who gets disability benefits, and the Patent and Trademark Office to decide who gets patent protection.

Still other agencies are developing AI tools to communicate with the public, by sifting millions of consumer complaints or using chatbots to field questions from welfare beneficiaries, asylum seekers and taxpayers.

Our research also highlights AIs potential to make government work better and at lower cost. AI tools that help administrative judges spot errors in draft decisions can shrink backlogs that leave some veterans waiting years (sometimes, close to a decade) for benefits. AI can help ensure that the decision to launch a potentially ruinous enforcement action does not reflect the mistakes, biases, or whims of human prosecutors. And AI can help make more precise judgments about which drugs threaten public health.

But the picture is not all rosy.

First, the government has a long way to go. Our teams computer scientists found that few agency AI uses rival the sophistication found in the private sector, making it harder to realize accuracy and efficiency gains. Some may wish to keep agencies low-tech to limit surveillance or otherwise hamstring government. Its not that simple: Government use of makeshift and insecure AI systems puts everyone at risk. Disabled persons, veterans and all of us deserve better.

Second, AI poses deep accountability challenges. When public officials make decisions affecting rights, the law generally requires an explanation. This reason-giving requirement is deeply embedded in law and even enshrined in the Constitution. Yet sophisticated AI tools are opaque; they do not serve up explanations with their outputs. A crucial challenge is how to subject these tools to meaningful accountability and ensure fidelity to longstanding commitments to transparency, reason-giving and non-discrimination.

To address concerns, agencies could be required to politically ventilate AI tools the way they must new regulations. Or they could be made to benchmark AI tools, reserving a pool of cases for human decision and comparing results to AI-assisted ones. However, there are no one-size-fits-all solutions. Open-sourcing computer code might make sense when agencies distribute welfare benefits. But disclosing details when tax enforcers use AI to identify cheaters will just aid evasion.

Third, if we want agencies to make responsible use of AI, their capacity must come from within. Our research shows that many of the best-designed AI tools were created by innovative, public-spirited agency technologists not profit-driven private contractors. The AI tools that help adjudicate disability benefits at the Social Security Administration came from agency insiders with intimate knowledge of governing law and how administrative judges work.

This makes sense. Government work is often complex. Recruiting skilled technologists and updating outmoded computing systems is crucial to building high-quality AI tools and administering them fairly. But it wont be cheap.

Last, AI can fuel political anxieties. Government AI use creates a risk of gaming by better-heeled groups with resources and knowhow. The SECs algorithmic predictions may fall more heavily on smaller companies that, unlike big Wall Street players, lack a stable of quants who can reverse-engineer the model and keep out of the agencys cross-hairs. If citizens come to believe AI systems are rigged, political support for a more effective, tech-savvy government will evaporate.

In short, this is a pivotal moment for government. Managed well, agency AI use can make the government more efficient, accurate and fair. Managed poorly, AI can widen the public-private technology gap, make agencies more vulnerable and less transparent, and heighten concerns about government arbitrariness and biases that are coursing through American politics.

Wherever the nation lands on facial recognition, government AI use is here to stay. The question now is which of these two visions becomes reality.

David F. Engstrom and Daniel E. Ho are professors of law at Stanford University. Catherine M. Sharkey is a professor of law at New York University. Mariano-Florentino Cullar is a justice on the California Supreme Court and professor of law at Stanford University.

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What to do about artificially intelligent government | TheHill - The Hill

DUBLIN--(BUSINESS WIRE)--The "Enterprise Artificial Intelligence Market: Global Opportunity Analysis And Industry Forecast, 2019-2026" report has been added to ResearchAndMarkets.com's offering.

According to this report, the global enterprise artificial intelligence market was valued at $4.68 billion in 2018, and is projected to reach $53.06 billion by 2026, registering a CAGR of 35.4% from 2019 to 2026.

Artificial intelligence has been one of the fastest growing technologies in recent years. AI is associated to human intelligence with similar characteristics, such as language understanding, reasoning, learning, problem solving, and others. Manufacturers in the market witness enormous underlying intellectual challenges in the development and revision of such technology. AI is positioned at the core of the nextogen software technologies in the market. Companies, such as Google, IBM, Microsoft, and other leading players, have actively implemented AI as a crucial part of their technologies.

The increase in number of innovative start-ups and advancements in technology have led to rise in investment in artificial intelligence technologies. Moreover, escalating demand for analyzing and interpreting large amount of data boosts the requirement of artificial intelligence industry solutions. Moreover, development of more reliable cloud computing infrastructures and improvements in dynamic artificial intelligence solutions have a strong impact on the growth potential of the AI market. However, lack of trained and experienced staff hinders the growth of the enterprise Artificial Intelligence (AI) market. Furthermore, increase in adoption of AI in developing economies, such as China, and India are expected to provide major opportunities for the market growth in the upcoming years. Also, ongoing developments in smart virtual assistants and robots are anticipated to be opportunistic for the growth of the enterprise artificial intelligence (AI) market.

KEY BENEFITS

Key Topics Covered:

Chapter 1: Introduction

1.1. Report Description

1.2. Key Benefits For Stakeholders

1.3. Key Market Segments

1.4. Research Methodology

1.4.1. Secondary Research

1.4.2. Primary Research

1.4.3. Analyst Tools & Models

Chapter 2: Executive Summary

2.1. Cxo Perspective

Chapter 3: Market Overview

3.1. Market Definition And Scope

3.2. Key Findings

3.2.1. Top Investment Pockets

3.2.2. Top Impacting Factors

3.3. Porter'S Five Forces Analysis

3.4. Key Player Positioning, 2017

3.5. Market Dynamics

3.5.1. Drivers

3.5.1.1. Increasing Investment In Ai Technologies

3.5.1.2. Growing Need For Analyzing And Interpreting Large Amounts of Data

3.5.1.3. Increasing Customer Satisfaction And Adoption of Reliable Cloud Applications

3.5.2. Restraints

3.5.2.1. Lack of Trained And Experienced Staff

3.5.3. Opportunities

3.5.3.1. Increase In Adoption of Ai In Developing Economies

3.5.3.2. Developing Smarter Virtual Assistants And Robots

3.6. Market Evolution/ Industry Roadmap

Chapter 4: Global Enterprise Artificial Intelligence (Ai) Market, By Deployment Type

4.1. Market Overview

4.2. Cloud

4.2.1. Key Market Trends, Growth Factors, And Opportunities

4.2.2. Market Size And Forecast, By Region

4.2.3. Market Analysis, By Country

4.3. On-Premise

4.3.1. Key Market Trends, Growth Factors, And Opportunities

4.3.2. Market Size And Forecast, By Region

4.3.3. Market Analysis, By Country

Chapter 5: Global Enterprise Artificial Intelligence (Ai) Market, By Technology

5.1. Market Overview

5.2. Machine Learning

5.3. Natural Language Processing (Nlp)

5.4. Image Processing

5.5. Speech Recognition

Chapter 6: Global Enterprise Artificial Intelligence (Ai) Market, By Organization Size

6.1. Market Overview

6.2. Large Enterprises

6.3. Small And Medium Enterprises (Smes)

Chapter 7: Global Enterprise Artificial Intelligence (Ai) Market, By Industry Vertical

7.1. Market Overview

7.2. Media & Advertising

7.3. Bfsi

7.4. It & Telecom

7.5. Retail

7.6. Healthcare

7.7. Automotive & Transportation

7.8. Others

Chapter 8: Global Enterprise Artificial Intelligence (Ai) Market, By Region

8.1. Market Overview

8.2. North America

8.3. Europe

8.4. Asia-Pacific

8.5. LAMEA

Chapter 9: Competitive Landscape

9.1. Competitive Dashboard

9.2. Key Developments

9.3. Top Winning Strategies

Chapter 10: Company Profiles

10.1. Alphabet Inc.

10.2. Apple Inc.

10.3. Amazon Web Services, Inc.

10.4. International Business Machines Corporation

10.5. Ipsoft Inc.

10.6. Microstrategy Incorporated

10.7. Nvidia Corporation

10.8. Sap Se

10.9. Verint Systems Inc.

10.10. Wipro Limited

For more information about this report visit https://www.researchandmarkets.com/r/w3dpp3

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Global Enterprise Artificial Intelligence Market Expected to Grow with a CAGR of 35.4% Over the Forecast Period, 2019-2026 - ResearchAndMarkets.com -...

Over 40% of privacy compliance technology will rely on artificial intelligence (AI) by 2023, up from 5% today, according to Gartner, Inc. Privacy laws, such as General Data Protection Regulation (GDPR), presented a compelling business case for privacy compliance and inspired many other jurisdictions worldwide to follow, said Bart Willemsen, research vice president at Gartner.

More than 60 jurisdictions around the world have proposed or are drafting postmodern privacy and data protection laws as a result. Canada, for example, is looking to modernize their Personal Information Protection and Electronic Documents Act (PIPEDA), in part to maintain the adequacy standing with the EU post-GDPR.

Privacy leaders are under pressure to ensure that all personal data processed is brought in scope and under control, which is difficult and expensive to manage without technology aid. This is where the use of AI-powered applications that reduce administrative burdens and manual workloads come in.

AI-Powered Privacy Technology Lessens Compliance Headaches

At the forefront of a positive privacy user experience (UX) is the ability of an organization to promptly handle subject rights requests (SRRs). SRRs cover a defined set of rights, where individuals have the power to make requests regarding their data and organizations must respond to them in a defined time frame.

According to the 2019 Gartner Security and Risk Survey, many organizations are not capable of delivering swift and precise answers to the SRRs they receive. Two-thirds of respondents indicated it takes them two or more weeks to respond to a single SRR. Often done manually as well, the average costs of these workflows are roughly $1,400 USD, which pile up over time.

The speed and consistency by which AI-powered tools can help address large volumes of SRRs not only saves an organization excessive spend, but also repairs customer trust, said Mr. Willemsen. With the loss of customers serving as privacy leaders second highest concern, such tools will ensure that their privacy demands are met.

Global Privacy Spending on Compliance Tooling Will Rise to $8 Billion Through 2022

Through 2022, privacy-driven spending on compliance tooling will rise to $8 billion worldwide. Gartner expects privacy spending to impact connected stakeholders purchasing strategies, including those of CIOs, CDOs and CMOs. Todays post-GDPR era demands a wide array of technological capabilities, well beyond the standard Excel sheets of the past, said Mr. Willemsen.

The privacy-driven technology market is still emerging, said Mr. Willemsen. What is certain is that privacy, as a conscious and deliberate discipline, will play a considerable role in how and why vendors develop their products. As AI turbocharges privacy readiness by assisting organizations in areas like SRR management and data discovery, well start to see more AI capabilities offered by service providers.

For more information, visit http://www.gartner.com

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Compliance technology will rely on artificial intelligence in the future - ELE Times