Category Archives: Ai

Companies planning on using artificial intelligence (AI) in their work should ensure it is transparent and accountable, the Information Commissioners Office (ICO) has said.

The UKs data watchdog has published its first draft regulatory guidance into the use of AI in collaboration with the Alan Turing Institute.

It warned that the public are still uneasy over the use of computer software to make decisions previously made by humans, so any systems must be transparent and provide clear explanations of decisions made.

The guidance identified four key principles for AI: transparency, accountability, consideration of context and reflection on impacts.

The ICO said it had found that more than half of people remain concerned about machines making complex, automated decisions about them.

The potential for AI is huge, but its implementation is often complex, which makes it difficult for people to understand how it works, said Simon McDougall, the ICOs executive director of technology and innovation.

And when people dont understand a technology, it can lead to doubt, uncertainty and mistrust.

Last year, ministers published the AI Sector Deal, a joint venture between the Government and industry to try to push the UK to the forefront of emerging technology such as AI.

The ICO and the Alan Turing Institutes draft guidance comes after an independent review by Professor Dame Wendy Hall and also the Government urged both parties to provide input on the subject.

The guidance said the four main principles are rooted in the General Data Protection Regulations (GDPR), EU-wide laws introduced last year to hand greater control over personal data to individuals.

The principles say organisations should ensure decisions made by AI are obvious and appropriately explained to people in a meaningful way.

On accountability, it says firms should ensure appropriate oversight of AI decision systems, and be answerable to others.

It also called for companies to reflect on the impact their AI use would have by ensuring they ask and answer questions about the ethical purposes and objectives of your AI project at the initial stages of formulating the problem and defining the outcome.

The ICO said it will consult on its guidance until January 24, and Mr McDougall encouraged industry experts to respond to its draft before then.

The decisions made using AI need to be properly understood by the people they impact, he said.

This is no easy feat and involves navigating the ethical and legal pitfalls around the decision-making process built in to AI systems.

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Artificial intelligence use 'must be transparent and accountable' - The Irish News

02 December 2019

A quarter of UK jobs in artificial intelligence and other specialist technology roles were filled by women this year, the lowest proportion in two decades, according to research from Datatech Analytics for professional network Women in Data.

The research showed that the percentage of women taking on positions related to data science, out of the overall number of people entering the industry, fell from 41% of the total in 2005, to 34% in 2009 and then to 27% this year.

According to the research, the drop in the proportion of women working in these roles is due to a surge in men choosing career fields such as AI over the past 19 years, a 400% increase, compared to a 68% uptick for women during the same period.

Payal Jain, who chairs the Women in Data campaign, said this could appear "fairly bleak reading for us", but said, because of that, "women are some of the most sought after talent in the UK right now" and that there is no better time to be a woman in data."

Kelly Metcalf, Head of Diversity, Inclusion and Wellbeing at Fujitsu UK & Ireland, also commented, Diverse teams allow organisations to provide environments where different styles of thinking come together, allowing for more innovation and productivity, so its a concern to see such a small proportion of artificial intelligence roles being filled by women. With no signs of digital transformation slowing down, it is becoming increasingly important that all businesses not just technology organisations do more to tackle this gap and attract a diverse range of talent.

Ultimately, there are many steps organisations can implement to encourage a diverse and inclusive work environment to ensure that the UK sustains its technology top spot, Metcalf said. And for women in the UK, if organisations are to deliver genuine change they must commit to a big vision that diversity and inclusion produces much better results.

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UK Proportion of women in AI and data jobs hits 20-year low, research finds - Staffing Industry Analysts

Advanced technologies in robotic process automation (RPA) such as AI, machine learning, cognitive computing and pattern matching have been transforming how we manage projects. Whether you are a program manager responsible for the portfolio of initiatives in healthcare or a technical project manager overseeing the redeployment of an enterprise resource planning (ERP) solution, you need to understand key elements of advanced technologies such as machine learning and AI so you can spend less time on planning and more time on execution.

As a project manager who has led project management offices for Fortune 500 organizations for over 12 years, I've experienced firsthand the various iterations of this trade transformation. Having credentials such as PMP, ACP and Six Sigma is a proven way to demonstrate your commitment to project management as a profession, but I am also a strong believer that adapting and staying ahead of the latest trends in technology will help you continue to deliver value as a project manager.

Here, I'll provide a series of recommendations on best practices and skills required to effectively apply and use advanced technologies for better project delivery. Automation or other advanced technologies such as AI will not replace project management as a practice, but project managers need to evolve and continuously develop their technical skills in order to use these technologies in conjunction with the interpersonal elements of managing projects.

Indeed, there are plenty of areas of the project management or software development life cycle (SDLC) that could be automated, but as we revisit the first proponent of the Agile Manifesto, "Individuals and interactions over processes and tools."

Critical skills for project managers in advanced technologies

Project managers should continuously enhance their technical expertise and skill set. Even if you're a nontechnical PM who is working with the business side of the project, you would still benefit greatly from being able to have a meaningful conversation with the development team. RPA technologies such as Automation Anywhere, Blue Prism and UiPath can provide solid frameworks to get introduced to key principles.

Understanding the key principles in the application or system design methodologies can help you manage your projects more efficiently. For instance, when analyzing the proposed solutions for a given design architecture, you can evaluate the overall durations and resources required for a given set of tasks and provide recommendations on estimated timelines and resource capacity.

Some high-level examples of system design methodologies that can provide a framework for further research include object-oriented analysis and design (OOAD) a method or a framework for designing a business process or a system through visual modeling and one of the popular techniques that the project managers should be familiar with when advancing their skills in RPA domain-driven design (DDD) and layered application development with layers such as application/presentation, data, services and business.

There are various courses and certifications that may provide for additional knowledge as well as credibility in RPA.

Resource management with AI and ML

Resource planning and resource capacity modeling are the most critical phases in the project management life cycle. Many companies have well-defined databases of specific roles and preestablished templates with MS Project for project managers, which provide for a solid framework to analyze the data for potential automation. Whether you need to crash the schedule or fast-track it, AI can be a powerful tool in creating predictive models based on the historical performance of similar tasks. However, this would require a diligent and consistent initiative to create a historical database of all past successful and failed projects.

RPA relies on historical data, and project managers need to drive the closing phases of each project with due diligence. Consequently, when certain repetitive tasks are automated, your staff is left with more time to make project-centric decisions that positively influence its delivery.

There are various recommendations for project managers who are taking on projects in advanced technologies or looking to transition into leading the companywide transformation initiatives. Whether you are leading a retrospective session with your Agile team or a lessons-learned committee upon completion of a project, it is imperative to create a repository of historical data used in each project.

RPA and AI should both go through the five lean principles: identify value, map the value stream, create the flow, establish pull and seek perfection. Each PM who is looking to adopt RPA should aim to adapt to some of these principles of lean in order to create a more robust framework for managing RPA projects.

In summary

Whether you are leading a small upgrade project for the latest software update or spearheading a companywide ERP redeployment initiative, identifying opportunities for automation and process improvement is critical for future success. We continuously identify tools and methods for project managers such as templates for resource capacity planning and modeling or a predefined set of automated tasks on project plans as well as define critical-to-quality (CTQ) skills for PMs when sourcing for the latest candidates for an assigned project.

Project managers play an integral part in driving these initiatives and making sure that each phase of the project life cycle is consistently adhered to. Without the historical data and lessons learned, RPA and other advanced technologies will be challenging to implement and adapt.

Originally posted here:

How To Use RPA And AI For Project Management - Forbes

Amazon Web Services Inc.s big re:Invent conference officially kicked off today in Las Vegas and the cloud giant has wasted no time today, announcingan array of services and features headlined by new artificial intelligence offerings.

First up is Amazon Transcribe Medical, an AI transcription service that enables medical professionals to dictate notes and record patient conversations. Its AWS attempt to free up some of the several hours per day that the average primary care physical spends on administrative tasks such as data entry.

Amazon Transcribe Medical is provided as an application programming interface that can be plugged into healthcare services. Its compliant with the HIPAA medical data protection regulation, automatically adds pronunciation marks into transcribed text where appropriate and, according to AWS, supports virtually any device that has a microphone. That means developers couldtheoreticallybring it tosmart speakers such as Amazons Echo devices, which are already finding usein some medical settings.

SageMaker is an AWS service that allows developers to build and train AI models withoutmanaging theinfrastructure below. Now, software teams can carry outSageMaker projects via Kubernetes thanks to a set of new operators that the cloud giant is rolling out.

Each Amazon SageMaker Operator for Kubernetes provides you with a native Kubernetes experience for creating and interacting with your [SageMaker] jobs, either with the Kubernetes API or with Kubernetes command-line utilities such as kubectl, AWS senior product manager Aditya Bindal explained in a blog post.

SiteWise, AWS service for analyzing data from industrial equipment, is getting a new visualization tool that displaysoperational information in graphs. Its available as a browser-based applicationunder the name SiteWise Monitor. A plants maintenance team can use it to build dashboards that shows how often each piece of equipment is down due to technical issues, while a business analyst at the same facility could visualize factory output.

SiteWise Monitorhas arrived as part of abroader update to the service that brings other analytics features, too. Chief among them is a new digital twins capability. Companies can now take sensor measurements from physical assets such as a production line and create a digital copy, or twin, of the hardware that lets analysts virtually experiment with new ways to optimize operations.

Copies of Windows Server and Linux running on AWS instances need to be updated when theres a new version available just like the operating system of a physical server. Administrators typically either perform the task manually or create custom scripts to automate the process, but AWS claims thatEC2 Image Builderoffers a better way.

The newly revealed service provides a graphical interface for handling operating system updates. Administrators can use EC2 Image Builder to customize Windows Server and Linux distributionupdatesas needed, testthe new version to see if works and then automatically deploy the update to their companies cloud environments.

Topping off the list of new offerings unveiled during AWS midnight announcement bonanza is DeepCompose, a AI-powered musical keyboard. The device allows developers to familiarize themselves with machine learning by building and training models that generate music. AWS now offers no fewer than three differentAI learning devices: DeepComposer joins the recently upgraded DeepRacerremote-controlled model car and the DeepLens camera.

Lastly, AWS also rolled out several smaller enhancements, including new features for managing software licenses companies use on its cloud platform.

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Musical keyboards and AI on Kubernetes: AWS fires off first salvo of re:Invent updates - SiliconANGLE

MRP Prelytix is an ABM Platform designed to power global enterprise programs. The technology and services aim to help identify the needs and buyer's journey stage of each target account and apply real-time AI to trigger orchestrations across seven channels, in 20 languages.

Prelytix offers a variety of tools to power lead intelligence, marketing campaigns and execution. Key features include:

Lead Intelligence:

Marketing Campaigns:

Campaign Execution:

MRP Prelytix's target audience includes mid-range to large enterprises that serve multiple geographies, lines of business or industries. These enterprises require a flexible, mature and scalable ABM solution that can support global companies and coordinate execution across multiple marketing channels.

While integrated custom engagement data includes hardened connections directly into SFDC, Siebel, Eloqua, REST API, Marketo, HubSpot and Pardot, MRP Prelytix enables client systems to behave like ABM tools, making ABM a consistent strategy across client teams and technology.

MRP Prelytixis delivered via software-as-a-service (SaaS) and licensed on an annual subscription basis and is customized to meet the needs and objectives of clients.

MRP works with more than 450 companies, including Oracle, HPE, SAP and Thomson Reuters, managing over 1,000 engagements across the globe.

For enterprise organizations that serve multiple geographies, lines of business or industries, MRP Prelytix is an ABM platform designed to give you control of your data, visibility into your target market and scale in the delivery of the highest impact engagement strategy.

MRP1818 Market Street37th FloorPhiladelphia, PA 19103Tel: 215-587-8800Email: marketing@mrpfd.com

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MRP Prelytix Is Designed To Power ABM Campaigns With Real-Time AI - Demand Gen Report

"Now you can build your AI app for free on Arterys and in minutes, instead of years, distribute them with the speed of the internet," said CEO and co-founder Fabien Beckers. The developer tools available on Arterys include the first online deployment environment with a radiologist viewer, a scalable backend, seamless integration capabilities, and support for both regulatory approved and experimental AI models. Now creators can focus on creating the best AI models and publishing full clinical applications online without having to spend years building them. "Because every radiologist's workflow is different, we're putting the power in their hands to give feedback and iterate with a community of AI developers."

Arterys has invested more than $50M and over seven years of development into building its proprietary internet platform and clinical-grade web viewer to take diagnostic imaging online. After building its own clinical AI applications, Arterys is making its platform available to a growing global community of AI innovators. Unlike others, the Arterys Marketplace is available to all meaning anyone can share their AI models via a simple URL, and anyone on the Arterys Marketplace can try it on their own medical images.

Arterys invites all developers to share their content on the Arterys Marketplace, regardless of where in the world or what stage of development they're in (research or regulated AI apps). The company doesn't exert editorial control over the content and provides a set of guidelines for best practices. Arterys encourages developers to use content and editorial curation to drive audience development and engagement with their AI.

"We're making the process of uploading, sharing, and testing your medical image models on external data as easy as uploading, sharing, and watching a YouTube video," said Arterys Marketplace Product Manager Christian Ulstrup. "We firmly believe innovation can come from anyone, anywhere in the world. That's why we're working hard to make the Marketplace the only open, frictionless, and user-driven medical image AI platform available."

The Arterys Marketplace will be on display at the company's booth at RSNA. Check it out at the RSNA 2019 AI Showcase, Booth #10918 or on our website: https://www.arterys.com/marketplace

Are you an AI model developer? Interested in taking part in the Arterys Marketplace closed beta? Arterys is taking applications through the end of the yearjoin the growing global community of AI developers working to bring deliver on the promise of AI in clinical practice by signing up at https://www.arterys.com/developers

SOURCE Arterys Inc.

http://www.arterys.com

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Arterys launches the first viewer-based AI Marketplace for medical imaging, fueling open innovation - PRNewswire

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The process repeats until the AI model reaches its desired accuracy.

This week at the annual conference of the Radiological Society of North America, machine learning and AI developers Nvidia unveiled its federated learning platform Clara. A system designed to protect patient privacy while still enabling medical centres to collaboratively train models and process patient data.

Due to the sensitive nature of medical data hospitals and medical centers, for both legal and privacy concerns, dont share images or data. Unfortunately, keeping all of this data in a silo means that no AI or ML models can be trained on it. Federated learning involves creating a central global server that sends a training algorithm to each medical centre taking part in the model training. Each institution trains the model on their private dataset, before sending it back to be aggregated by the central server. At no point does the sensitive data leave the medical centre.

Hospitals or medical centres using the Clara Federated Learning (Clara FL) system will label all of their data using an AI-assisted annotation SDK that is currently integrated in medical viewers such as 3D slicer, MITK and Philips Intellispace Discovery. This data is then trained on in-house servers before it is sent to the global server.

Nvidia commented in a release that: Clara FL is a reference application for distributed, collaborative AI model training that preserves patient privacy. Running on NVIDIA NGC-Ready for Edge servers from global system manufacturers, these distributed client systems can perform deep learning training locally and collaborate to train a more accurate global model.

Currently the University of California, Los Angeles is using Clara FL to introduce AI technology into its radiology department. Radiology departments produce a wealth of medical images captured from patients with a host of medical conditions, these include X-Rays, Computed tomography (CT scans) and magnetic resonance imaging (MRI) images.

Training an AI model on these images in order for it to spot patterns and help identify potential illness earlier in the diagnostic process has in the past been difficult due to patient privacy concerns, as such these medical images rarely leave the radiology department. With Clara AI and ML models can be trained without compromising patient privacy.

Clara FL operates using Nvidias EGX Edge platform, a high-performance platform that has been created to tackle the massive amounts of data created by modern technology. The EGX stack includes a driver, Kubernetes plug-in, container runtime plug-in and GPU monitoring software. Telcos can install all required Nvida software as containers that run on Kubernetes, giving flexibility. (The stack architecture is supported by Canonical, Cisco, Nutanix, Red Hat and VMware.)

Nvidia have also teased the release of Clara AGX an AI developer kit that aims to process high-data rate video and images that are flooding in from sensors embedded in medical devices.

Nvidia states that the: Clara AGX is powered by NVIDIA Xavier SoCs, the same processors that controls self-driving cars. They consume as little as 10W, making them suitable for embedding inside a medical instrument or running in a small adjacent system.

Originally posted here:

Privacy Concerns About Training AI On Medical Data, Nvidia Thinks Clara is the Answer - Computer Business Review

December 02, 2019 -Healthcare produces more data than any other industry, but mastering how to use data to be actionable while safeguarding sensitive data from unauthorized access can be a Sisophean effort. The double-digit year-over-year growth of the medical internet of things (IoT) coupled with a more data driven approach to patient care is accelerating the volume of sensitive healthcare data that could becoming vulnerable to misuse or thief. So, it should be of little surprise that traditional forms of identity management lack the agility to keep pace with health IT infrastructure changes. Fortunately, new forms of identity management leveraging artificial intelligence (AI) and machine learning (ML) can enable health systems, hospitals, and physician practices to remain productive and secure.

As Healthcare data collection accelerates, the need to safeguard who needs access and when is going to require new levels of sophistication and foresight that some healthcare entities do not currently possess. In general, less than 3% of a health system budget is spent on IT, says SailPoint Vice President of Healthcare Matthew Radcliffe. Outside of their core clinical, the investment in IT has not kept pace with the speed of growth and expansion, but if we make identity management processes more efficient through the lens of healthcare; healthcare organizations can reprioritize IT resources and invest more in patient care.

For many healthcare organizations, there is a process gap onboarding new clinicians. A lag in enabling clinicians to have data access creates operational inefficiencies that can impact patient care. And while data security is a top of mind issue for healthcare organizations, most organizations do not have the security expertise, bandwidth, or know-how to rapidly onboard clinicians seamless. Over the last decade or so, healthcare organizations have come to realize that they need more effect processes to manage access to patient data within enterprise systems by developing an identity governance program, Radcliffe continues.

To enable a clinician with first-day access to those systems they need what we call birthright access, the access they need on day one to treat a patient the sooner they can begin treating patients, the sooner the hospital can realize the operating benefits of a physician/clinician doing their job and the revenue associated with treating patients, adds Radcliffe.

Turning AI into ROI

New forms of identity management enabled by the cloud and powered by AI and ML have the potential to eliminate inefficiencies such as, access gaps that impact clinical and operational workflows without introducing risks to health data security and privacy.

Process gaps and administrative error can impact clinical and operational productivity and workflows. However, a predictive, AI-driven approach that automates the identity management processes can improve operational efficiencies. The more systems are automated, the level of error and inefficiencies are removed from the process. By removing redundancies, organizations can securely and efficiently give staff access all while improving identity governance.

It's very easy to demonstrate an ROI around productivity and how these solutions can help staff gain access sooner and more securely explains Gianni Aiello, Director of Product Management at SailPoint.

We have been solving these problems for a long time, but in some cases the way tools were used led to over entitled access that users did not use or need. This was the result of a focus on productivity over security. The outcome was higher risk of a breach and the potential for massive fines. AI and ML approaches can help us reduce the potential risk of incorrect access by 1030%. Whilst also improving productivity around governing access by up to 60%. This represent a huge return for healthcare organizations.

The challenge of present-day identity management is one of bandwidth. Healthcare is transforming so fast that humans can't keep up with ever-changing user populations, the rapid need for access, and more importantly the need to govern that access, Radcliffe stresses. Now consider marrying the transformation challenge with the rapid increase and use of of connected devices in healthcare connected IoT devices, infusion pumps, heart monitors, and smart beds as the most common examples. These connected devices are generating enormous amounts of data, and there's no way a human would be able to respond to these dynamics without leverging efficient and automated governance platforms . But the use of machine learning for identity management is able to turn current data into actionable information in two areas: access automation and regulatory compliance.

Healthcare organization need a convenient way to have staff log-on/log-off of shared clinical desktops and historically, to authenticate users, most organization would leverage the tap in tap out single sign-on method and this is where healthcare-based identity management programs would start and stop. Clinicians were historically enabled with these types of access management solutions without first understanding the specific role the clinician would serve within the organization, determinging if the clinician had previous access that could potentially conflict with net-new access or if the user should even be enabled with access due to some level of security policy conflict . As the number of users and systems have expentionally grown, there is greater need for healthcare organizations to establish a full identity management program. Innovative healthcare organizations should broaden their identity management program principles by adapting identity governance, data governance, privileged access management, and enterprise single sign-on as a full identity program.

In reality, the amount of data that needs to be collected, and ultimately looked at and analyzed, is huge, says Aiello. Looking for that needle in a haystack, for a human, is quite frankly nearly impossible. It's just not achievable. And so, machine learning is, in real terms, the only way you can start to better see and understand how people are using their access.

Unlike early machine learning and AI applications that were mainly rule-based approaches, were looking at specific scenarios that can be solved by discovered risk not pre-determined rules notes Aiello. That knowledge can be transferred to how you ultimately model access for the efficiency of staff around what they need to have access to do that job, Aiello states. Providing snapshots of access is a step toward homing in on appropriate access rights and ultimately determining how to grant and manage access moving forward.

Improving regulatory compliance

The Health Insurance Portability and Accountability Act (HIPAA) Security Rule requires that an individual or entity accessing protected health information (PHI) electronically be authenticated before access is granted. Unlike other industries, health systems, hospitals, and physician practices that have a data breach are often faced with consequences that go beyond significant fines, e.g., an erosion of patient and clinician trust.

Federal and state regulations for reporting health data security and privacy are increasing; and the task of maintaining healthcare data compliance will continue to be a daunting, labor intensive administrative process that will require consistent organizational commitment and vigilance. By automating access rights and augmenting decisions processes, organizations can leverage their data to help reduce the burden of user compliance.

At SailPoint we catalog access that is inherently low risk and doesnt expose data to inappropriate functions or allow for a clinician to see data that's inherently risky to the organization, Aiello reveals.

Automating user access creates a building block for more intelligent decision-making around identity management and governance. As healthcare focuses on their mission of driving operational and clinical efficiencies to improving patient outcomes, an AI-enabled identity management solutions can be strategic investment that can evolve with an organizations current and future IT roadmaps.

Minimize costs and complexity

With a drive towards digitalization, many healthcare organizations are leveraging the prodigious amount of sensitive data for decision making. When it comes to leveraging data to drive better patient outcomes, healthcare leaders are vacillating between innovation and compliance.

Effective identity management is critical to data governance. As such, healthcare organizations can lay the groundwork to ensuring that increasing data access doesn't lead to exponential growth in risk.

This is a business opportunity for healthcare organizations, Radcliffe advises. The way they see the opportunity to grow their business is to obtain access to more data and more patients beyond the brick-and-mortar hospital. This means broader access to digital records with the aim of caring for patients across the continuum of care.

We have to infuse integrated identity and data governance platforms into the digitization of healthare while leveraging AI and machine learning to keep up with the pace of healthcare business transformation, Radcliffe concludes.

To improve patient outcomes and driving operational efficiencies, healthcare organizations should invest in an AI-powered identity management solution for future operational success.

________________________________________

About SailPoint

SailPoint enables healthcare provider organizations to cost-effectively protect healthcare data, reduce financial risk from poor audit performance, and avoid disruptions to patient care. Infused with artificial intelligence, its predictive identity governance anticipates how access should change, shows where attention is needed, and recommends actions. Ideally suited for healthcare, the SailPoint platform increases IT and operational efficiencies by automating processes and simplifying the on-boarding and management of complex healthcare user populations (employees, affiliated physicians, contractors and others). SailPoint is consistently recognized by Gartner, Forrester and KuppingerCole as the leading authority on identity governance, and the preferred partner for numerous healthcare organizations.

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Minimize Costs and Complexity With AI-Powered Identity Management - HealthITSecurity.com

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

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.

Read more:

The Artificial Intelligence Industry and Global Challenges - Forbes