Category Archives: Ai

Kim Constable, Founder at The Sculpted Vegan

She speaks with a lilting Irish accent yet there is no mistaking the power behind the tough talk and the incredible abs that have thrust Kim Constable into the multi-million dollar bracket with her platform The Sculpted Vegan.

As a 38-year old mother of four, living a charmed life in her native Belfast, Kim Constable took a good hard look at herself in the harsh light of day and decided the thing she was missing in her life was muscles. Growing up in a rural village outside of Belfast, she says, my Dad subscribed to bodybuilding magazines, and I had always admired the power behind those incredible physiques, she explains.

Kim had been a vegetarian for nigh on 16 years but had decided to go full vegan about a year before deciding to train.

There was one small problem, however. Her trainer explained that he couldnt help with the nutrition side of her program because he had zero experience training vegans. Determined to find answers, she took to Google and found there was nothing out there to help shape her program.

She decided to make one, and make millions of dollars in the process.

Bodybuilders, vegan or not, track everything. The reason we track is because you cannot know which variables to manipulate progress or change if you havent kept the data consistent. We track our calories, macros, sleep, recovery, cardio duration, heart rate, weight, and measurement.

When she started training as a vegan bodybuilder, Kim bought an Apple Watch.

It was the first time I had ever tracked anything to do with health, and the data was interesting. The Apple watch was one of the first trackers on the market, but it seems rudimentary now, compared to what is available. It only really tracked my steps and workouts daily and gave me virtual high fives when I had achieved a goal.

When the Oura Ring launched Kim switched to using this device, and says its by far the best shes worn. The tracking is superb, and it only needs to be charged every 5 days for about 20 minutes. I never take it off. It syncs with my Apple watch to track my workouts and the step tracker is far more accurate than Apple seems to be.

According to Kim, data measurement is not only how she tracks performance And improved results, it is key to her success.

Rest and recovery are paramount to my success. My Oura Ring reminds me that bedtime is approaching and suggests a wind-down routine. It also suggests bedtimes, which I try to adhere to. Unfortunately, I cant take it easy when it suggests, if it conflicts with my training program. But just seeing the data daily is important as I can make subtle changes to my routine, to ensure Im getting enough rest.

The scariest part of tracking health data, says Kim, is seeing the effect that alcohol has on your body. It keeps your resting heart rate high, and your heart rate variability low. Seeing the data in front of you makes you more mindful.

She doesnt sugarcoat her program or her success. Its hard, and I tell people that right upfront. I tell them, dont buy my program unless youre willing to put in the work.

Scrolling through her Instagram page @thesculptedvegan, filled with incredible photos of her bodybuilding achievements, Kim tells it like it is. One image of her showing off her amazing abs features the caption, it took four years to get these abs. Over years of building exceptional muscle with alternating periods of shredding, finally my abs started to appear.

Her advice is pretty simple; she has no time for victims. If you make up your mind to do something, it takes work, consistency, and discipline, she says. Those are the hallmarks of her success she doesnt try to make it look easy.

Data helps Kim and her clients stay on track and stay accountable. One of Kims favorite sayings is: What gets measured, gets managed.

Its easy to ignore the possible effects of behavior when they are not in front of your face. Tracking using AI keeps the data forefront, and allows you to make better and more informed decisions.

For women bodybuilders, keeping track of data can be especially useful. At home, Kim has a Renpho Smart Scale which tracks weight, body fat, body fat mass, and other variables. Kim says this piece of equipment is not as intelligent as the Oura and doesnt make suggestions for your training or progress, but its extremely useful to track the data over an extended time, especially around your monthly cycle.

For bodybuilders, tracking weight daily allows you to see the fluctuations over time, especially when all other variables are kept the same. During the month, my body will slowly hold more and more water leading up to my period. I will also hold more water after alcohol or salty food, says Kim.

You learn not to make it mean anything its just data. And it takes all emotion out of weight gain.

The other thing Kims Oura Ring tracks is her daily temperature, so she can see how it rises during the month and peaks during her cycle. This is usually when my sleep is the most restless, says Kim. I can also push more load mid-month when my temperature is lower and my recovery is stronger. Oura helps me with all of this tracking.

Kim Constable, Founder at The Sculpted Vegan

Kims data tracking of course would not be very useful without incredible time management skills and self-discipline to do it all. I have a husband and four kids who are home-schooled. If I commit to training five days a week, I do it. If I say Im going to make a million dollars from my business, I do it.

In a recent Forbes article, the author writes: In some ways, an AI-driven fitness coach can be better than a human trainer. It has access to more data, knows more exercises, and can track your progress more precisely.

In response, Kim states, I dont believe that it could ever replace a coach standing in front of you, intuitively advising based on what they see. One of the ways humans process data is through intuition. It has been studied extensively and shown to be alarmingly accurate.

While Kim acknowledges that data-tracking and AI-suggested adjustments have been a key aspect of her fitness (and business) success, she has fears that people would become too reliant on the data and stop trusting their intuition, learning deductively rather than inductively.

As a bodybuilder, I have a very strict workout timetable. I train 5 days a week, and cardio 6 days. If my Oura Ring tells me my recovery is bad and I should rest, but its my day for training legs, Im not going to give legs a miss. AI can respond to your body on a day-to-day basis, but unless it can help you to accomplish a specific goal in the future, and therefore tell you to push through even when you dont feel like it, its going to work against you and not for you.

Kim continues, AI is great. But it has its limitations. Unless its able to help you to work towards a specific goal, which often involves pushing through your tiredness or poor recovery just to get the work done, then it would have serious limits for professional athletes or the non-average person.

While her personal goals to build an incredible body and inspire the women who follow her align with her strong moral code, she continues to seek out the uncomfortable situations that inform her writing, her posts, and her videos.

Bodybuilding and business have a lot in common; they both involve hard work and often failure. I love failure; I call it learning. Even though I stand firmly behind my goals, Im not afraid to change lanes if something isnt working.

Creating a roadmap for women to build confidence, muscle, and success while being brutally honest about what that will involve is part of her appeal. She is not for every woman, but she believes every woman can be her if they are willing to put in the work and never give up.

To many privileged individuals in our society, these data tracking devices for fitness are readily available. Once relegated to billion-dollar science labs, sports tech is now available for us to buy at the local Costco. However, having a watch or ring that tracks your data and gives you suggestions is just one part of the equation.

Kim concludes, What you do with that data is up to you.

Excerpt from:

Utilizing AI To Reach Peak Performance In Health And Business: How The Sculpted Vegan Built An 8-Figure Empire - Forbes

Chatbot On Mobile App

When the pandemic required that retailers shut their doors, consumers still needed a way to reach the brands they use and interact with every day. Due to the need to go remote, the contact center quickly took the place of in-person retailing, enabling consumers to connect to brands by phone, chat, email and more.

Contact centers had long been run on-premise, but these resources also had to change to accommodate the remote nature of daily life, and many retailers quickly shifted those systems to the cloud. In addition to enabling agents to service customers remotely, the cloud-enabled contact center also allows brands to harness data from all those interactions and make them smarter and more effective.

I recently connected with Genefa Murphy, CMO of Five9, an intelligent cloud contact center solution, who spoke to me about how brands are reimagining the customer experience and the technology they use to facilitate it. She explained that AI is a massive part of that, helping retailers to connect with their customers more successfully beyond chatbots, across all channels.

Gary Drenik: Customer service has been hugely important for companies during the pandemic. What types of channels are consumers relying on most?

Genefa Murphy: The pandemic has driven a surge of customer service traffic to the digital store front of enterprises. When customers couldnt engage with businesses in person, they increasingly looked to digital channels, such as email, web chat, SMS, and social messaging, to resolve their requests before picking up the phone. New research from Prosper Insight & Analytics exploring the importance of various services when shopping online found that 81% of adult consumers believe its very important to have a website thats easy to use, while just over half (52%) rate toll free live assistance as very important. The importance of an easy-to-use website is even greater among Gen X (83%) and Boomers (84%) according to the research.

Prosper - Importance of Services When Shopping Online

With more consumers reaching out across more channels than ever before, businesses are looking for solutions to help them quickly scale their service teams to keep up with the demand. They are not just deploying AI-powered self-service applications like Intelligent Virtual Agents (IVAs) to automate the more routine and repetitive service tasks. They are also looking to AI and automation to help with dynamic interaction routing, matching customer demand with contact center supply. In some cases that supply comes in the form of a live agent, in many cases that comes from IVA and online digital support. The key thing is that customers are asking for these channels, and they are fast becoming the norm. In fact, our own research has found that 82% of consumers are now comfortable using digital channels. Therefore, the change has moved from a push to a pull.

Drenik: What about chatbots? Why arent they as popular as some may have anticipated just a few years ago?

Murphy: The first generation of chatbots were somewhat underwhelming because most of them lacked the Natural Language Understanding (NLU) to intelligently process and respond to customer requests. This in turn led to more frustration. However, as conversational AI technology has matured, chatbots have evolved from that limited scope. They can now handle more complex tasks and transactions and actually provide a better customer experience. Today, many chatbots have the intelligence and context they need to respond in a much more conversational manner. The tooling and training of AI have also improved. This is important because it enables chatbots to engage with greater accuracy. Given this, we are starting to see a major uptick in chatbot usage and value for both the business and the end customer.

Drenik: Are there any other channels where AI is playing a big role currently? Which ones?

Murphy: AI is not just for digital channels; AI is being increasingly adopted over the voice channel. Consumers are getting used to having conversations with their smart devices, and the same technology that powers those devices is now more available and accessible in the cloud. In fact, Gartner predicts that by 2023,customers will prefer to use speech interfacesto initiate 70% of self-service interactions, rising from 40% in 2019. Today, companies can launch cost-effective IVAs to provide conversational self-service over the phone with minimal investment and a faster time to value than ever before.

Were also seeing AI play a big role in agent-facing support applications. Intelligent assistants can utilize machine learning and Natural Language Processing to automate call transcriptions and notetaking, quickly surface the data agents need to resolve customer requests or even to drive more business and provide real-time coaching and reminders that can improve the overall customer experience.

Drenik: How is AI playing a role in these channels?

Murphy: AI is enabling businesses to reimagine the kind of customer experience they can provide across voice, web, and messaging channels. IVAs allow businesses to quickly scale their service operations, empowering customers to engage on their channel of choice any time of day or night and resolve many requests without waiting for live assistance. As a result, live agents have more time to focus on conversations where they create value by applying their human touch. Additionally, because IVAs integrate so easily into CRM systems, knowledgebases and other back-end systems, service interactions can be more personalized and escalate across every support channel more seamlessly.

For all these reasons, AI-powered service automation is helping businesses boost interaction handling efficiencies, customer loyalty, agent satisfaction, and overall cost savings and revenue. This is especially true when IVAs are connected into the broader contact center eco-system so that context and data can be preserved especially when there is a need to escalate to a live agent.

Drenik: Whats important for businesses to know about using AI in their customer service strategies?

Murphy: Businesses can reap huge rewards by implementing AI across the customer journey, as long as they choose a practical approach to deploying and managing the technology. AI isnt pixie dust. They should look for platforms and tools that are self-managed, simple and intuitive, and that will easily integrate across the channels and back-end-systems that are most essential to their customer journey. They should also adopt strategies that allow them to implement these solutions incrementally, so they achieve quick wins, drive tangible results and solve real business challenges. We call this practical AI.

The most forward-thinking service organizations understand the potential of AI and automation to work alongside their human teams, rather than as a means of replacing them to cut costs. The workforce of the future is multi-modal.It works across channels, it leverages AI and automation, its digital and human, and it enables work from anywhere. Using AI to automate more of the routine, repetitive service tasks, and to provide real-time assistance during calls can also go a long way to improve the agent experience, which is a key factor in providing great CX especially as we all know employee experience often reflects in the customer experience.

Drenik: Thank you, Genefa, for taking the time to share your insights on how AI is helping brands to better serve their customers across channels. Its exciting to learn about the ways businesses are using technology to help bolster the customer experience.

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Not Just Chatbots: How AI Is Helping Organizations Better Serve Their Customers - Forbes

Amazons Alexa has a voice familiar to millions: calm, warm, and measured. But like most synthetic speech, its tones have a human origin. There was someone whose voice had to be recorded, analyzed, and algorithmically reproduced to create Alexa as we know it now. Amazon has never revealed who this original Alexa is, but journalist Brad Stone says he tracked her down, and she is Nina Rolle, a voiceover artist based in Boulder, Colorado.

The claim comes from Stones upcoming book on the tech giant, Amazon Unbound, an excerpt of which is published here in Wired. Neither Amazon nor Rolle confirmed or denied Stones reporting, which he says is based on conversations with the professional voiceover community, but Rolles voice alone makes for a compelling case.

Listen to the videos below: the first an advertisement for Cherry Creek North, Denvers premier outdoor retail destination, and the second an introduction to Hapyn, a social app that seems to now be defunct (its Play Store entry was last updated in 2017). You can absolutely hear Alexas reassuring tones in Rolles voice. Or, to be more precise, you can absolutely hear where Alexas reassuring tones come from when listening to Rolle.

Heres how Stone writes up the process in selecting Alexas voice:

Believing that the selection of the right voice for Alexa was critical, [then-Amazon exec Greg] Hart and colleagues spent months reviewing the recordings of various candidates that GM Voices produced for the project, and presented the top picks to Bezos. The Amazon team ranked the best ones, asked for additional samples, and finally made a choice. Bezos signed off on it. Characteristically secretive, Amazon has never revealed the name of the voice artist behind Alexa. I learned her identity after canvasing the professional voice-over community: Boulder, Coloradobased voice actress and singer Nina Rolle. Her professional website contains links to old radio ads for products such as Motts Apple Juice and the Volkswagen Passatand the warm timbre of Alexas voice is unmistakable. Rolle said she wasnt allowed to talk to me when I reached her on the phone in February 2021. When I asked Amazon to speak with her, they declined.

Weve pinged Amazon and Rolle to confirm her involvement in creating Alexa, but dont expect to hear much back. If the company isnt interested in confirming Stones account, it suggests this is a bit of history theyd rather not draw attention to, for whatever reason.

Providing the voice for such a ubiquitous product can have its own drawbacks, too. The original voice artist behind Siri, Susan Bennett, revealed herself in 2013 (after seeing an article from The Verge dissecting the process behind the creation of synthesized voices, incidentally) but said shed been wary about being associated with Siri. I was conservative about it for a long time [...] then this Verge video came out [...] and it seems like everyone was clamoring to find out who the real voice behind Siri is, and so I thought, well, you know, what the heck? This is the time, Bennett told CNN.

Of course, although we can hear both Bennett and Rolles voices in their AI doppelgngers, its impossible to say without inside knowledge exactly what traces of the original remain. Creating a synthetic voice starts with real audio samples, but this data is exhaustively quantized and remastered to such a degree that answering the question of whether the final product is the same as the original is best reserved for the shipbuilders of Theseus.

What is, fun, though, is listening to the other examples of Rolles voiceover work on her website. Although she offers a restrained performance in the videos above, shes much more animated and lively in other commercial samples. It really shows how, despite the ever-increasing sophistication of Alexas voice, it still lacks the range of the real thing.

Excerpt from:

Meet the real Alexa: voice actor reportedly responsible for Amazons AI assistant revealed - The Verge

A survey of senior UK and European IT chiefs, conducted by Morning Consult for IBMs Global AI adoption index 2021, has found that deployments of artificial acceleration (AI) are accelerating.

The study, based on a survey of 2,500 senior decision-makers from the UK, France, Germany, Italy and Spain, found that one-third (36%) reported that their companies had accelerated their roll-out of AI as a direct result of the Covid-19 pandemic. However, only 27% of UK IT professionals reported that their company had accelerated the roll-out of AI in response to the virus.

Significantly, 38% of the IT decision-makers from the UK who took part in the survey said their employer had made no change to their adoption of the technology as a response to the global health crisis, compared with a European average of 33%.

However, there is general recognition among IT decision-makers that AI has a role to play in supporting organisations as they plot a path through post-pandemic business recovery. Many see AI as a way to enhance competitiveness and streamline productivity through the use of automation tools.

The study reported that automating processes to empower higher-value work was the single biggest reason for the adoption of AI across Europe picked by 43% of the study group. The numbers for the UK were lower, with 35% of UK IT decision-makers selecting it as a reason to invest in AI.

Jean Philippe Desbiolles, global vice-president, data and AI at IBM, said 2021 will deliver a real AI return on investment for businesses. We are at a juncture point, he said. AI deployment is now here and there is clear acceleration. Across Europe and the UK, the study found that 44% of organisations that have run AI pilots plan to deploy AI.

However, the increasing complexity of data is a significant roadblock for widespread adoption. In the survey, almost a quarter (24%) of UK IT decision-makers identified increasing data complexity and the existence of siloed data as barriers to adoption, compared with 29% of the European IT decision-makers.

The study also found that the proliferation of data across the enterprise has resulted in six out of 10 UK IT professionals drawing from more than 20 different data sources to inform their AI.

There is general consensus among the survey respondents that there is a lack of expertise in AI. One-third (33%) of the IT decision-makers surveyed said their organisations planned to upskill their workforce. There is also growing interest in off-the-shelf AI to help organisations address the skills gap and overcome the technical challenges of infusing AI into business processes.

Desbiolles said: From my experience, the focus has been to demonstrate some real AI use cases. This has driven the market, but for the first time, I can see that the market now wants business AI off the shelf.

He added that IBMs customers and prospects wanted to see the business impact of AI more than ever, and were moving beyond the experimental phase of AI to production deployments that generate business value.

As an industry, this is where we need to adapt to deliver platforms, tools and also the right business applications, he said.

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UK lagging in post-Covid AI-driven business recovery - ComputerWeekly.com

Artificial intelligence (AI) has transformed many aspects of our lives for the better. It even played a role in developing vaccines against COVID-19. But you may be surprised just how many things we take for granted that rely on AI.

As IBM explain, "at its simplest form, artificial intelligence is a field, which combines computer science and robust datasets to enable problem-solving." It includes the sub-fields of machine learning and deep learning. These two fields use algorithms that are designed to make predictions or classifications based on input data.

This is how AI is used in our everyday lives.

Image: European Parliament

Of course, as technology becomes more sophisticated, literally millions of decisions need to be made every day and AI speeds things up and takes the burden off humans. The World Economic Forum describes AI as a key driver of the Fourth Industrial Revolution.

Forecasted shipments of edge artificial intelligence (AI) chips worldwide in 2020 and 2024, by device.

Image: Statista

The Forums platform, Shaping the Future of Technology Governance: Artificial Intelligence and Machine Learning, is bringing together key stakeholders to design and test policy frameworks that accelerate the benefits and mitigate the risks of AI and machine learning.

Here are 10 examples of AI we encounter every day.

Your email provider almost certainly uses AI algorithms to filter mail into your spam folder. Quite helpful when you consider that 77% of global email traffic is spam. Google says less than 0.1% of spam makes it past its AI-powered filters.

But there are concerns that algorithms that read content to target advertising are invading our privacy.

AI automates a host of functions on your smartphone, from predictive text that learns the words you commonly use to voice-activated personal assistants which listen to the world around them and try to learn your keywords.

The way your phone screen adjusts to ambient light or the battery life is optimized is also down to AI. But if the personal assistant absorbs everything you say, whether youre on the phone or not, some critics say it creates opportunities for surveillance, however benign the intention.

In many parts of the world, online and app-based banking are the norm. From onboarding new customers and checking their identity to countering fraud and money laundering, AI is in charge. Want a loan? An AI-powered system will assess your creditworthiness and decide.

This is how AI is used in banking.

Image: Business Insider

AI also monitors transactions and AI chatbots can answer questions about your account. More than two-thirds of banks in a recent survey by SAS Institute say they use AI chatbots and almost 63% said they used AI for fraud detection.

Going for an x-ray? Forget the idea of a clinician in a white coat studying the results. The initial analysis is most likely to be done by an AI algorithm. In fact they turn out to be rather good at diagnosing problems.

In a trial, an AI algorithm called DLAD beat 17 out of a panel of 18 doctors in detecting potential cancers in chest x-rays.

However, critics say AI diagnosis must not become an impenetrable black box. Doctors need to know how they work in order to trust them. Issues around privacy, data protection and fairness have also been raised.

As in banking, chatbots are also being deployed in healthcare to engage with patients - for example, to book an appointment - or even as virtual assistants to physicians. This presents numerous issues though, from miscommunication to wrong diagnoses.

The World Economic Forum's Chatbots RESET programme brings together stakeholders from multiple areas to explore these opportunities and challenges to govern the use of chatbots.

AI is at the heart of the drive towards autonomous vehicles, adoption of which has accelerated due to the pandemic. Delivery services are one area being targeted, while China now has a robotaxi fleet operating in Shanghai.

There are still safety issues to be ironed out, however. There have been accidents involving self-driving cars, some of them fatal.

The Netherlands is the best prepared for autonomous cars.

Image: Statista

Conventional trackside railway signals are being replaced by AI-powered in-cab signalling systems which automatically control trains. The European Train Control System allows more trains to use the same stretch of track while maintaining safe distances between them.

To date, the use of AI in controlling aircraft has been limited to drones, although flying taxis that use AI to navigate have already been flight-tested. Experts say a human is still better at flying an airliner but AI is widely used in route planning, optimizing schedules and managing bookings.

7. Ride sharing and travel apps

Ride sharing apps use AI to resolve the conflicting needs of drivers and passengers. The latter want a ride immediately, while drivers value their freedom to start and stop working when they choose. Learning how these patterns interact, AI can send you a ride when you ask for it.

Travel apps use AI to personalize what they offer users as algorithms learn our preferences. Hotel search engine Trivago even bought an AI platform that customizes search results based on the users social media likes.

Uncanny how social media seems to know what you like, isnt it? Of course, its all down to AI. Facebooks machine learning can recognize your face in pictures posted on the platform, as well as everyday objects to target content and advertising that interests and engages you.

Job seekers using LinkedIn benefit from AI which analyzes their profile and engagement with other users to offer job recommendations. The platform says AI is woven into the fabric of everything that we do.

Unexpected breakdowns are every factory managers nightmare. So AI is playing a key role in monitoring machine performance, enabling maintenance to be planned rather than reactive. Experts say its cutting the time machines are offline by 75% and repair costs by almost a third.

AI can also predict changes in demand for products, optimizing production capacity. AI is currently used in about 9% of factories worldwide but Deloitte says 93% of companies believe AI will be a pivotal technology to drive growth and innovation in the sector.

Google says AI can enhance the value of wind power by 20%.

Image: Pixabay/enriquelopezgarre

10. Regulating power supply

Wind and solar power may be green but what happens when the wind doesnt blow and the sky is cloudy? AI-powered smart technology can balance supply and demand, controlling devices like water heaters to ensure they only draw power when demand is low and supply plentiful.

Googles DeepMind created an AI neural network trained using weather forecasts and turbine data to predict the output from a wind farm 36 hours ahead. By making output to the power grid more predictable, Google says it increased the value of its wind energy by 20%.

The views expressed in this article are those of the author alone and not the World Economic Forum.

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Did you know these 10 everyday services rely on AI? - World Economic Forum

SEATTLE, May 11, 2021 /PRNewswire/ -- Outreach, the largest and fastest-growing sales engagement provider, announcedthree new products that expand the definition of sales engagement further than ever before. By focusing on real-time conversation intelligence, buyer sentiment, and buyer engagement, Outreach now supports sellers across the entire revenue cycle while capturing rich buyer behavioral data that provides managers and revenue leaders with greater visibility into pipelines and forecasts.

"In a world that has changed so dramatically, it's what you do now that matters," said Manny Medina, CEO and co-founder of Outreach. "That's why we built real-time conversation and made revenue intelligence actionable. Because we must empower revenue organizations to continuously evolve so they can thrive in this new environment. We're living up to our vision of reimagining the category and paving the way for a new buyer and seller engagement model."

At Unleash 2021, before an audience of thousands of sales practitioners and industry thought leaders, Outreach unveiled Outreach Kaia, Outreach Insights, and Outreach Success Plans, all designed to support sales reps, managers, and leaders:

Outreach Kaiaprovides sellers with in-the-moment coaching, leading to faster seller ramp times, shorter deal cycles, and better buyer experiences. Leveraging industry-leading artificial intelligence (AI), Kaia is a voice-and video-enabled virtual sales assistant that surfaces relevant sales enablement content based on real-time buyer prompts, transcribes conversations with more than 90% accuracy, and captures notes and action items. By providing real-time coaching, capturing notes and next steps, Kaia makes every sales rep more effective and every customer conversation more productive.

Outreach Insightshelps sellers better understand the level of interest a prospect or customer has in engaging in a sales conversation. Outreach Insights is an integrated reporting and analytics solution that leverages cutting-edge AI to detect buyer sentiment and more accurately measure buyer emotion, allowing sellers to optimize what, when, and how they communicate with their buyers. Sentiment analysis represents a significant leap forward relative to legacy and ineffective activity metrics like reply, click, and open rates, which are incorrect 50% of the time.

Outreach Success Plansallows sellers and buyers to collaborate on the Outreach platform to create dynamic action plans leading to mutually successful business outcomes. The first-of-its-kind product, now available in public beta, coordinates and aligns sell-side and buy-side teams involved in the deal. Data captured from these interactions will provide revenue leaders with the ability to course-correct deals that are off track and forecast with more accuracy.

"Over the last 15 months, as revenue leaders strived to meet and exceed targets amid a global pandemic, they faced the most challenging environment of their careers. As the economy bounces back, those leaders who made bets on the right technologies and providers are thriving, while those that didn't are missing a huge opportunity," said Mary Shea, global innovation evangelist at Outreach. "Having a comprehensive technology platform and the right partner is now more essential than ever. By integrating real-time conversation intelligence, buyer sentiment analytics, and a dynamic environment that enhances buyer and seller collaboration, Outreach is transforming the sales engagement category while delivering unparalleled value to its users across the entire revenue cycle."

For more information about Outreach, including product demos, images, and videos, please visit http://www.outreach.io/GameOn.

About Outreach Outreach is the largest and fastest-growing sales engagement platform that helps companies dramatically increase productivity and drive smarter, more insightful engagement with their customers. The only sales engagement platform to make the Forbes Cloud 100, Outreach was also the fastest-growing Sales Engagement Platform on the Deloitte Technology Fast 500. More than 4,600 companies such as Adobe, Tableau, Okta, Splunk, DocuSign, and SAP depend on Outreach's enterprise-scale, unparalleled customer adoption, and robust AI-powered innovation. Outreach is a privately held company based inSeattle, Washington. To learn more, please visitwww.outreach.io.

PR Contact: Amanda Woolley [emailprotected]

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Outreach Propels Sales Engagement Forward with Integrated AI-Powered Conversation Intelligence, Buyer Sentiment Insights, and Success Plans, Unveiled...

Nichols Martin May 11, 2021News, Technology

Department of Veterans Affairs

The Department of Veterans Affairs' National Artificial Intelligence Institute is sharing its expertise with other agencies to develop a catalog of AI use cases, FedScoop reported Monday.

The institute has shared input with the Veteran Engagement Board, the Data Governance Council and other federal agencies to create a reference for AI uses.

Gil Alterovitz, VA's director of AI, said at the SNG Live: Enhancing AI in Government event that the department plans to trial a set of modules that can support an AI-focused internal review board.

Agencies have engaged in AI data sharing as part of efforts to align with Former President Trump's executive order on trustworthy AI made in late 2020.

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AeroVironment announced on Tuesday that its Board of Directors has unanimously elected Wahid Nawabi to succeed Tim Conver as chairman upon Convers retirement effective at the companys 2021 annual meeting of shareholders. Tims leadership and mentorship have resulted in countless American and allied lives saved as a result of our innovative UAS and TMS solutions, concluded Nawabi.

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AI-Focused Veterans Affairs Organization Shares Best Practices With Other Agencies - ExecutiveGov

"With StockGeist.ai, you can quickly feel the spirit of the most recent developments in the business world."

StockGeist is a web-based platform that uses deep learning models to provide a convenient, real-time monitor of the sentiment and context behind developments in the stock market as reflected in the media and social media. StockGeist.ai's intuitive interface lets users quickly build customized watchlists with companies of interest to observe the dynamics in their rankings and track other up-to-date information.

"With StockGeist.ai, you can quickly feel the spirit of the most recent developments in the business world," said Dr. Vytautas Abramavicius, StockGeist.ai team lead from Neurotechnology. "Our web platform aggregates a tremendous amount of data from various media and social media sources. Using modern deep learning-based NLP (Natural Language Processing) models, StockGeist.ai allows users to derive meaningful insights from noisy data. It reflects our best efforts to bring this information to investors and traders in an intuitive and efficient way, so they can make faster decisions regarding stocks of interest," Abramavicius added.

StockGeist.ai was first released as a free-to-use platform on October 7, 2020. Since then, a number of new features have been added:

For more information about StockGeist.ai, please visit: http://www.stockgeist.ai.

About NeurotechnologyNeurotechnologyis a developer of high-precision algorithms and software based on deep neural networks and other AI-related technologies. The company was launched in 1990 in Vilnius, Lithuania, with the key idea of using neural networks for various applications, such as biometric person identification, computer vision, robotics and artificial intelligence. Since the first release of its fingerprint identification system in 1991, the company has delivered more than 200 products and version upgrades. More than 3,000 system integrators, security companies and hardware providers in more than 140 countries integrate Neurotechnology's algorithms into their products. The company's algorithms have achieved top results in independent technology evaluations, including NIST MINEX, PFT, FRVT, IREX and FVC-onGoing.

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Neurotechnology Releases New Version of StockGeist.ai Platform for Real-Time Monitoring of Publicly Traded Companies - PRNewswire

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I've written before about semantic ambiguity in natural languages and how difficult, if impossible, it would be to render natural languages into a digital object.

The reasons: the gestalt of a silicon processor versus the human brain and brain-to-brain communication:

Something isambiguouswhen it can be understood in two or more possible senses or ways. If theambiguityis in a single word, it is called lexicalambiguity. ... In everyday speech,ambiguitycan sometimes be understood as something witty or deceitful.

Today, the terms trustworthy AI and ethical AI are used interchangeably. The problem is that trustworthy AI is not necessarily ethical, and ethical AI is necessarily trustworthy. The casual commingling of the terms has unfortunate circumstances

Let's break down trust and trustworthiness. There is a difference between 'trust,' which can be described in pretty straightforward factual terms and 'trustworthiness,' which is a very different matter and has a value component. It is about what or whoshouldbe trusted. Unfortunately, we can both trust those who are not trustworthy, and not trust those who are. Trust and transparency go together: we can only trust what we are clear is being asked of us.

Ethics determines whether a strategy should be chosen because it seeks simple utilitarian terms to secure the best overall aggregate balance of harms and costs. Or whether it rests on a belief that there are fundamental human rights that should never be sacrificed. Values inform a judgment of a proper or proportionate balancing of the loss of individual liberty and privacy for the gain of certain public goods; or whether it is fair to expect some social and age groups to suffer disproportionately in any public health initiative.

Here is one attempt to define ethical AI:

Organizations ready to embrace AI and thrive in the Age of With must start by putting trust at the center. First, they must thoroughly assess whether their organization meets the criteria for trustworthy and ethical AI; it's a necessary step in increasing the returns and managing the risks that constitute the transformational promise of AI.

This is messed up. Trust is something given based on transparency, reputation, and sometimes, unfortunately, blind faith in wholly untrustworthy characters. It is not a consummate good. Putting trust at the center implies ethics are of secondary concern.

Here are a few examples of trustworthy but potentially unethical models:

Predictive Policing: City government is in an endless cycle of allocation of resources for all of the things they have to do. One area that gets cut is policing. In an attempt to introduce some element of fairness (or at least science) to how to deploy and redeploy policing, they have turned to several AI solutions that predict where police need to be. Implementing these systems shows a matter of trust in the operation and outcome, but the experience has led to unethical outcomes.

The models themselves are, for the most, free from bias, but they calculate occurrences of crimes in segments of the city and assign more policing. The problem is, though organized to fight Class1 crimes, homicides, arson and assaults, more boots on the ground begin to pick up more Class 2 crimes, such as vagrancy, trespass, curfew or hold a small number of drugs. As this data flows back to the system in an infinite feedback loop, more police are assigned, and crime rates soar. Moreover, since these phenomena occur in neighborhoods with a high degree of color, the result is entirely unethical.

Intrusive personalization: Giving your most initiate information to ad servers and marketers, clicking through websites, ordering online, talking to Siri, people tend to trust these applications even though subsequent use of the data can be highly unethical in persuasion, digital phenotyping and disrupting civil society.

Life insurance: life insurance is the paradigm of trust. Their commercials of "good hands," "the future is safe," partners afterlife too," "for a secure life." The assumption when purchasing life insurance is that the face value will be distributed promptly to your beneficiaries. There are circumstances, clearly elucidated in the contract, where that would not happen, such as suicide in the first two years, or acts of war. But two-year-exclusion doesn't offer complete protection. Another exclusion is the matter of "material representations on the application." Effectively, it provides the insurance company the right to deny the death claim on material representations, which can be minor:

The cynical part of this is that Insurers typically do not investigate these situations at the beginning. Still, when a claim is large, or just beyond the two years, they will dig into thousands of sources to invalidate a claim and return the premium plus interest, but not the death benefit. This is an example of the perception of a trustworthy instrument: "If I die, my family will be taken care of," which is, in fact, an unethical process.

Ethical, but not trust trustworthy: one prominent example of ethical but not trustworthy is the use of machine learning in radiology. After some early gaffes when Stanford Medical's radiation oncology model produces noticeable result between different ethnic groups, they went back to the drawing board. They developed a system that identified tumors that most of a panel of radiologists did not, and the degree of false-positive and negatives was evenly distributed across groups. They'd developed an ethical system, cleansed of bias, but trust was a different issue. First of all, doctors are a conservative bunch. Many refused to accept the result. Then there was an unanticipated problem. As they licensed the software to other hospitals, the accuracy of the system dropped dramatically. The reason was that Stanford had state-of-the-art imaging technology that other hospitals did not. Trust in the system plummeted, and took some time to regain.

Let's drop "trustworthy" as a criterion for ethical AI. Ethics are about knowing what to do and doing it. Trust is about what or whoshouldbe trusted or how to create trust, whether or not it's ethical. Though they are commingled in specific ways, pursuing trust to the exclusion of ethics is dangerous.

Continue reading here:

Trustworthy AI versus ethical AI - what's the difference, and why does it matter? - Diginomica

Artificial Intelligence

A regular message in this column is that artificial intelligence (AI) wont spread widely until its easier to use than the requirement to have programmers who can work at the model level. That challenge wont be solved instantly, and its slowly changing. While technical knowledge is still too often required, there are ways in which development time can be shortened. One way thats been happening has been is the increased availability of pre-built models.

A few years back, a tech CEO loved to talk about the Cambrian Explosion of deep learning models, as if a lot of models meant real progress in the business world. It doesnt. What matters is the availability of useful models useful for business. In the usual meaning of the clich, the 80/20 paradigm still matters for business. While a large number of models might be of interest to academics, a much smaller subset will provide significant value to people attempting to gain insight in the real world.

In an attempt to help companies not have to recreate the wheel, ElectrifAi has built a body of AI models that can be called by applications. Those models are identified by use case, so developers can quickly narrow down options and chose to test models close to their needed use. I first became aware of the company when it issued a press release about entering the marketplace on Amazon SageMaker. They are also on the Google Cloud Marketplace.

Having worked with other companies using major sources marketplaces, I was curious. While there are still long term questions about such marketplaces, including how acquisitions of companies might impact partner applications on those marketplaces, it was important to find out more.

One key issue about buying models is the fact that privacy is increasingly important. Yet another company seeing data could be a compliance weak spot. We build and support models for our customers, said Luming Wang, CTO, ElectrifAi. However, our business model is that we dont see their data and they dont see our code. While we pre-structure and partially train models, we provide support and services that help customers tune models to their own use with their own data without us needing to see any information. Outside of those marketplaces, the company also works with systems integrators and other partners who work with their clients in implementation.

Mentioned earlier was the customers being able to choose appropriate models. That also extends to the fact that when AI is mentioned, were not only discussing deep learning. The models are built in a variety of AI techniques, including rules engines, xgboost, and neural networks (deep learning). Different domains require different techniques, said Mr. Wang. Still, rules engines can work seamlessly with neural networks for complex problems. Over one hundred rules, a neural network has advantages. In between, depending on context and data, either technique or other technologies can be used.

Given the focus on building a library of models for business, it is no surprise that very few of the models have a UI to present their own data. The models are accessed as function calls by the controlling applications. This is a key step in the evolution of AI accessibility. There needs to be some AI knowledge in order to evaluate the appropriate models to choose, but once that decision has been made, non-AI programmers only need to understand the calls and then can use the results in the wrapper application to address the business solution.

This attitude is excellent for the current state of AI in business. This presents AI not as something scary, or something requiring expensive and unique personnel, but rather as another easy to call function that can be accessed quickly by existing programmers working so solve a problem. The more programmers can access AI by calls, without having to know the details of a neural network or random forest, the faster AI will spread through the corporate technology infrastructure.

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Artificial Intelligence Models For Sale, Another Step In The Spread Of AI Accessibility - Forbes