Daily Archives: November 28, 2021

Dynamics 365 Sales Insights empowers sellers to deliver personalized engagement and build profitable relationships.Capabilities include:

Sales accelerator (preview)

Supercharge sales with a prioritized list of everything that needs to get done, and optimize the sales cadence for different types of prospects with sequences.

Premium assistant

Extend the power of the assistant by creating and customizing insight cards for your sales team with the assistant studio.

Predictive lead and opportunity scoring

Increase revenues with lead and opportunity scores that help sellers identify and prioritize the deals that are most likely to convert.

Premium forecasting

Unlock forecast predictions and snapshots, so you can better project your revenue and manage your pipeline.

Conversation intelligence

Automatically transcribe and analyze customer sentiment, conversation content, and speaking style, so you can understand and replicate successful strategies across your wholeteam.

Relationship analyticsFocus on the right customers by using intelligence and signals from Office 365 and Dynamics 365 that reveal relationship health and risks.

Who knows whomShow sellers the colleagues who are already interacting with a prospect and can provide an effective introduction based on Office 365 activities.

Talking pointsStart conversations and build rapport with personalized conversation starters based on previous communications.

Notes analysisIncrease seller productivity with contextual prompts that suggest new records to create, such as contacts and activities, based on intelligence gleaned from notes entered by sellers.

Currently available in the United States and EMEA. AI models for natural language processing, such as those used in Talking points and Notes analysis, are for English only.

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Dynamics 365 Sales Insights

How to enable to Workaround? The workaround is active as soon as AIGTC is running Does it interfear with AIGFP traffic? No, but if you only want live AI in the Sim you can stop AIGFP traffic via the settings,

First of all, a huge welcome to Aerosoft for joining the AI Traffic party in MSFS, Simple Traffic is a great product for XBOX users and users that just want traffic in their Sim and do not care about the

Hey Guys, thanks for talking an early part in our long journey to provide the same AI setup of P3D to MSFS. To be honest we are all overwhelmed by the total numbers of users we already have thank

More then a year after the release of MSFS and almost three years after the first AIG AI Manager beta it is time for the next major step the release of AIM 1.1 as public beta with included MSFS

Before starting with the public beta there are some information you should know. Setup P3D If you want to upgrade to the beta and already have Aim 1.0 running, download the BETA version and replace the old files with the

In preperation for the public bete test of AIG AI Manager for MSFS we have updated our AIGTC for MSFS. Version 0.5 will be working with MSFS and P3Dv4 and v5 on the same system. Compared to the P3D Version

Over the last months the tool has been tested by our team and some external testers and it seems more or less stable now. Users of P3D and AIM 1.0 will see that this Beta of AIM has currently less

Since the first announcement of AIGTC and AIGPLN back in late 2019 a lot have changed in the FS world. P3D has got a new big update, and MSFS was released. All these changes had an impact on the initial

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Alpha India Group

Hear from CIOs, CTOs, and other C-level and senior execs on data and AI strategies at the Future of Work Summit this January 12, 2022. Learn more

Artificial intelligence (AI), machine learning (ML) and other emerging technologies have potential to solve complex problems for organizations. Yet despite increased adoption over the past two years, only a small percentage of companies feel they are gaining significant value from their AI initiatives. Where are their efforts going wrong? Simple missteps can derail any AI initiative, but there are ways to avoid these missteps and achieve success.

Following are four mistakes that can lead to a failed AI implementation and what you should do to avoid or resolve these issues for a successful AI rollout.

When determining where to apply AI to solve problems, look at the situation through the right lens and engage both sides of your organization in design thinking sessions, as neither business nor IT have all the answers. Business leaders know which levers can be pulled to achieve a competitive advantage, while technology leaders know how to use technology to achieve those objectives. Design thinking can help create a complete picture of the problem, requirements and desired outcome, and can prioritize which changes will have the biggest operational and financial impact.

One consumer product retail company with a 36-hour invoice processing schedule recently experienced this issue when it requested help speeding up its process. A proof of concept revealed that applying an AI/ML solution could decrease processing time to 30 minutes, a 720% speed increase. On paper the improvement looked great. But the companys weekly settlement process meant the improved processing time didnt matter. The solution never moved into production.

When looking at the problem to be solved, its important to relate it back to one of three critical bottom-line business drivers: increasing revenue, increasing profitability, or reducing risk. Saving time doesnt necessarily translate to increased revenue or reduced cost. What business impact will the change bring?

Data can have a make-or-break impact on AI programs. Clean, dependable, accessible data is critical to achieving accurate results. The algorithm may be good and the model effective, but if the data is poor quality or not easy and feasible to collect, there will be no clear answer. Organizations must determine what data they need to collect, whether they can actually collect it, how difficult or costly it will be to collect, and if it will provide the information needed.

A financial institution wanted to use AI/ML to automate loan processing, but missing data elements in source records were creating a high error rate, causing the solution to fail. A second ML model was created to review each record. Those that met the required confidence interval were moved forward in the automated process; those that did not were pulled for human intervention to solve data-quality problems. This multistage process greatly reduced the human interaction required and enabled the institution to achieve an 85% increase in efficiency. Without the additional ML model to address data quality, the automation solution never would have enabled the organization to achieve meaningful results.

Each type of AI solution brings its own challenges. Solutions built in-house provide more control because you are developing the algorithm, cleaning the data, and testing and validating the model. But building your own AI solution is complicated, and unless youre using open source, youll face costs around licensing the tools being used and costs associated with upfront solution development and maintenance.

Third-party solutions bring their own challenges, including:

In highly regulated industries, these issues become more challenging since regulators will be asking questions on these topics.

A financial services company was looking to validate a SaaS solution that used AI to identify suspicious activity. The company had no access to the underlying model or the data and no details on how the model determined what activity was suspicious. How could the company perform due diligence and verify the tool was effective?

In this instance, the company found its only option was to perform simulations of suspicious or nefarious activity it was trying to detect. Even this method of validation had challenges, such as ensuring the testing would not have a negative impact, create denial-of-service conditions, or impact service availability. The company decided to run simulations in a test environment to minimize risk of production impact. If companies choose to leverage this validation method, they should review service agreements to verify they have authority to conduct this type of testing and should consider the need to obtain permission from other potentially impacted third parties.

When considering developing an AI solution, its important to include all relevant decision makers upfront, including business stakeholders, IT, compliance, and internal audit. This ensures all critical information on requirements is gathered before planning and work begins.

A hospitality company wanted to automate its process for responding to data subject access requests (DSARs) as required by the General Data Protection Regulation (GDPR), Europes strict data-protection law. A DSAR requires organizations to provide, on request, a copy of any personal data the company is holding for the requestor and the purpose for which it is being used. The company engaged an outside provider to develop an AI solution to automate DSAR process elements but did not involve IT in the process. The resulting requirements definition failed to align with the companys supported technology solutions. While the proof of concept verified the solution would result in more than a 200% increase in speed and efficiency, the solution did not move to production because IT was concerned that the long-term cost of maintaining this new solution would exceed the savings.

In a similar example, a financial services organization didnt involve its compliance team in developing requirements definitions. The AI solution being developed did not meet the organizations compliance standards, the provability process hadnt been documented, and the solution wasnt using the same identity and access management (IAM) standards the company required. Compliance blocked the solution when it was only partially through the proof-of-concept stage.

Its important that all relevant voices are at the table early when developing or implementing an AI/ML solution. This will ensure the requirements definition is correct and complete and that the solution meets required standards as well as achieves the desired business objectives.

When considering AI or other emerging technologies, organizations need to take the right actions early in the process to ensure success. Above all, they must make sure that 1) the solution they are pursuing meets one of the three key objectives increasing revenue, improving profitability, or reducing risk, 2) they have processes in place to get the necessary data, 3) their build vs. buy decision is well-founded, and 4) they have all of the right stakeholders involved early on.

Scott Laliberte is Managing Director of the Emerging Technology Group at Protiviti.

Excerpt from:

Is your AI project doomed to fail before it begins? - VentureBeat

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NYC aims to be first to rein in AI hiring tools - Bend Bulletin

By Jonny Lupsha, Current Events WriterDue to computational programming, artificial intelligence may seem like it understands issues and has a sense of moralitybut philosophically and scientifically is that possible? Photo By PopTika / Shutterstock

Many questions have arisen since the advent of artificial intelligence (AI), even in its most primitive incarnations. One philosophical point is whether AI can actually reason and make ethical decisions in an abstract sense, rather than one deduced by coding and computation.

For example, if you program into an AI that intentionally harming a living thing without provocation is bad and not to be done, will the AI understand the idea of bad, or why doing so is bad? Or will it abstain from the action without knowing why?

Researchers from a Seattle lab claim to have developed an AI machine with its own sense of morality, though the answers it gives only lead to more questions. Are its morals only a reflection of those of its creators, or did it create its own sense of right and wrong? If so, how?

Before his unfortunate passing, Dr. Daniel N. Robinson, a member of the philosophy faculty at Oxford University, explained in his video series Great Ideas of Psychology that the strong AI thesis may be asking relevant questions to solve the mystery.

Imagine, Dr. Robinson said, if someone built a general program to function that way, so the program could provide expert judgments on cardiovascular disease, constitutional law, trade agreements, and so on. If the programmer could then have the program perform these tasks in a way indistinguishable from human experts, the position of the strong AI thesis is that its programmers have conferred on it an expert intelligence.

The strong AI thesis suggests that unspecified computational processes can exist which then would sufficiently constitute intentionality due to their existence. Intentionality means making a deliberate, conscious decision, which in turn implies reasoning and a sense of values. However, is that really possible?

The incompleteness theoremGdels theoremsays that any formal system is incomplete in that it will be based on, it will require, it will depend on a theorem or axiom, the validity of which must be established outside the system itself, Dr. Robinson said. Gdels argument is a formal argument and it is true.

What do we say about any kind of computational device that would qualify as intelligent in the sense in which the artificial intelligence community talks about artificial intelligence devices?

Kurt Gdel developed this theorem with the apparent exception for human intelligence that liberates it from the limitations of his own theorem. In other words, Gdel believed there must be something about human rationality and intelligence that cant be captured by a formal system with the power to generate, say, an arithmetic.

If you accept that as a general proposition, then what you would have to say is that human intelligence cannot be mimicked or modeled on purely computational grounds, Dr. Robinson said. So, one argument against the strong AI thesis is that its not a matter of time before it succeeds and redeems its promises. It will never succeed and redeem its promises for the simple reason that the intelligence it seeks to simulate, or model, or duplicate, is, in fact, not a computationally-based [] intelligence.

Should the mystery ever be solved, we may finally be able to answer Philip K. Dicks question: Do androids dream of electric sheep?

Edited by Angela Shoemaker, The Great Courses Daily

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Seattle Researchers Claim to Have Built Artificial Intelligence That Has Morality - The Great Courses Daily News

Children are already interacting with AI technologies in many different ways: they are embedded in toys, virtual assistants, video games, and adaptive learning software. Their impact on children's lives is profound, yet UNICEF found that, when it comes to AI policies and practices, childrens rights are an afterthought, at best.

In response, the UN childrens agency has developed draft Policy Guidance on AI for Children to promote children's rights, and raise awareness of how AI systems can uphold or undermine these rights.

Conor Lennon from UN News asked Jasmina Byrne, Policy Chief at the UNICEF Global Insights team, and Steven Vosloo, a UNICEF data, research and policy specialist, about the importance of putting children at the centre of AI-related policies.AI Technology will fundamentally change society.

Steven Vosloo, a UNICEF data, research and policy specialist, by UNICEF

Steven Vosloo At UNICEF we saw that AI was a very hot topic, and something that would fundamentally change society and the economy, particularly for the coming generations. But when we looked at national AI strategies, and corporate policies and guidelines, we realized that not enough attention was being paid to children, and to how AI impacts them.

So, we began an extensive consultation process, speaking to experts around the world, and almost 250 children, in five countries. That process led to our draft guidance document and, after we released it, we invited governments, organizations and companies to pilot it. Were developing case studies around the guidance, so that we can share the lessons learned.

Jasmina Byrne AI has been in development for many decades. It is neither harmful nor benevolent on its own. It's the application of these technologies that makes them either beneficial or harmful.

There are many positive applications of AI that can be used in in education for personalized learning. It can be used in healthcare, language simulation and processing, and it is being used to support children with disabilities.

And we use it at UNICEF. For example, it helps us to predict the spread of disease, and improve poverty estimations. But there are also many risks that are associated with the use of AI technologies.

Children interact with digital technologies all the time, but they're not aware, and many adults are not aware, that many of the toys or platforms they use are powered by artificial intelligence. Thats why we felt that there has to be a special consideration given to children and because of their special vulnerabilities.

UNICEF/ Diefaga

Children using computers

Steven Vosloo The AI could be using natural language processing to understand words and instructions, and so it's collecting a lot of data from that child, including intimate conversations, and that data is being stored in the cloud, often on commercial servers. So, there are privacy concerns.

We also know of instances where these types of toys were hacked, and they were banned in Germany, because they were considered to be safe enough.

Around a third of all online users are children. We often find that younger children are using social media platforms or video sharing platforms that werent designed with them in mind.

They are often designed for maximum engagement, and are built on a certain level of profiling based on data sets that may not represent children.

Jasmina Byrne, Policy Chief at the UNICEF Global Insights team, by UNICEF

Predictive analytics and profiling are particularly relevant when dealing with children: AI may profile children in a way that puts them in a certain bucket, and this may determine what kind of educational opportunities they have in the future, or what benefits parents can access for children. So, the AI is not just impacting them today, but it could set their whole life course on a different direction.

Jasmina Byrne Last year this was big news in the UK. The Government used an algorithm to predict the final grades of high schoolers. And because the data that was input in the algorithms was skewed towards children from private schools, their results were really appalling, and they really discriminated against a lot of children who were from minority communities. So, they had to abandon that system.

That's just one example of how, if algorithms are based on data that is biased, it can actually have a really negative consequences for children.

Steven Vosloo We really hope that our recommendations will filter down to the people who are actually writing the code. The policy guidance has been aimed at a broad audience, from the governments and policymakers who are increasingly setting strategies and beginning to think about regulating AI, and the private sector that it often develops these AI systems.

We do see competing interests: the decisions around AI systems often have to balance a profit incentive versus an ethical one. What we advocate for is a commitment to responsible AI that comes from the top: not just at the level of the data scientist or software developer, from top management and senior government ministers.

Jasmina Byrne The data footprint that children leave by using digital technology is commercialized and used by third parties for their own profit and for their own gain. They're often targeted by ads that are not really appropriate for them. This is something that we've been really closely following and monitoring.

However, I would say that there is now more political appetite to address these issues, and we are working to put get them on the agenda of policymakers.

Governments need to think and puts children at the centre of all their policy-making around frontier digital technologies. If we don't think about them and their needs. Then we are really missing great opportunities.

Steven Vosloo The Scottish Government released their AI strategy in March and they officially adopted the UNICEF policy guidance on AI for children. And part of that was because the government as a whole has adopted the Convention on the Rights of the Child into law. Children's lives are not really online or offline anymore. And it's a digital life now.

This conversation has been edited for length and clarity. You can listen to the interview here.

UNICEF/ Schverdfinger

UNICEF has developed policy guidance to protect children from the potential impacts of AI

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Digital Child's Play: protecting children from the impacts of AI - UN News

Disinformation has been used in warfare and military strategy over time. But it is undeniably being intensified by the use of smart technologies and social media. This is because these communication technologies provide a relatively low-cost, low-barrier way to disseminate information basically anywhere.

The million-dollar question then is: Can this technologically produced problem of scale and reach also be solved using technology?

Indeed, the continuous development of new technological solutions, such as artificial intelligence (AI), may provide part of the solution.

Technology companies and social media enterprises are working on the automatic detection of fake news through natural language processing, machine learning and network analysis. The idea is that an algorithm will identify information as fake news, and rank it lower to decrease the probability of users encountering it.

From a psychological perspective, repeated exposure to the same piece of information makes it likelier for someone to believe it. When AI detects disinformation and reduces the frequency of its circulation, this can break the cycle of reinforced information consumption patterns.

However, AI detection still remains unreliable. First, current detection is based on the assessment of text (content) and its social network to determine its credibility. Despite determining the origin of the sources and the dissemination pattern of fake news, the fundamental problem lies within how AI verifies the actual nature of the content.

Theoretically speaking, if the amount of training data is sufficient, the AI-backed classification model would be able to interpret whether an article contains fake news or not. Yet the reality is that making such distinctions requires prior political, cultural and social knowledge, or common sense, which natural language processing algorithms still lack.

Read more: An AI expert explains why it's hard to give computers something you take for granted: Common sense

In addition, fake news can be highly nuanced when it is deliberately altered to appear as real news but containing false or manipulative information, as a pre-print study shows.

Classification analysis is also heavily influenced by the theme AI often differentiates topics, rather than genuinely the content of the issue to determine its authenticity. For example, articles related to COVID-19 are more likely to be labelled as fake news than other topics.

One solution would be to employ people to work alongside AI to verify the authenticity of information. For instance, in 2018, the Lithuanian defence ministry developed an AI program that flags disinformation within two minutes of its publication and sends those reports to human specialists for further analysis.

A similar approach could be taken in Canada by establishing a national special unit or department to combat disinformation, or supporting think tanks, universities and other third parties to research AI solutions for fake news.

Controlling the spread of fake news may, in some instances, be considered censorship and a threat to freedom of speech and expression. Even a human may have a hard time judging whether information is fake or not. And so perhaps the bigger question is: Who and what determine the definition of fake news? How do we ensure that AI filters will not drag us into the false positive trap, and incorrectly label information as fake because of its associated data?

An AI system for identifying fake news may have sinister applications. Authoritarian governments, for example, may use AI as an excuse to justify the removal of any articles or to prosecute individuals not in favour of the authorities. And so, any deployment of AI and any relevant laws or measurements that emerge from its application will require a transparent system with a third party to monitor it.

Future challenges remain as disinformation especially when associated with foreign intervention is an ongoing issue. An algorithm invented today may not be able to detect future fake news.

For example, deep fakes which are highly realistic and difficult-to-detect digital manipulation of audio or video are likely to play a bigger role in future information warfare. And disinformation spread via messaging apps such as WhatsApp and Signal are becoming more difficult to track and intercept because of end-to-end encryption.

A recent study showed that 50 per cent of the Canadian respondents received fake news through private messaging apps regularly. Regulating this would require striking a balance between privacy, individual security and the clampdown of disinformation.

While it is definitely worth allocating resources to combating disinformation using AI, caution and transparency are necessary given the potential ramifications. New technological solutions, unfortunately, may not be a silver bullet.

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Artificial intelligence may not actually be the solution for stopping the spread of fake news - The Conversation CA

VUNO will showcase four medical artificial intelligence solutions:VUNO Med-Chest X-Ray, VUNO MedLungCT AI,VUNO Med-DeepBrain andVUNO Med-BoneAge that can be seamlessly integrated with PACS(Picture Archiving and Communication System), EMR(Electronic Medical Record) systems or any medical devices. A virtual reading room linked to the PACS system will be set up in the VUNO booth so that visitorscan get a first-hand look at VUNO Med solutions that integrated with PACS.

VUNO will present the future R&D direction of VUNO at the AI Theater under the topic 'The Past, Present and the Future of VUNO Med for Precision Imaging.' Following that, nine research achievements will be presented at a research presentation session. It includes topics encompassing various medical imaging fields, such as MRI-based AI model in prediction of MCI to dementia, AI-aided diagnosis to diagnose lung nodule malignancy, basal lung metastasis in colon cancer patients.

Furthermore, VUNO will participate in IAIP (Imaging AI in Practice), a virtual demonstration program developed to effectively demonstrate the effects of AI solutions in clinical settings. The company will showcase VUNO MedLungCT AI on this program to demonstrate how it works by using real-world clinical scenarios.

Hyun-Jun Kim, CEO of VUNO said, "Through RSNA 2021 participation, we will introduce various clinical research achievements to global radiology officials using AI technology," Hyun-jun Kim, CEO of VUNO said, adding "we plan to expand our partnerships with overseas partner companies and medical institutions by offering an opportunity for participants to directly experience VUNO Med solutions."

SOURCE VUNO Inc.

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VUNO Boasts of its AI Solutions and Research Results at RSNA 2021 - PRNewswire