Yes, we’re still playing Wordle, even if we’ve dialed back the obsessive sharing of results on Facebook. Regular players have their strategies and favorite words to kick off the game to maximize their chances. As data scientists, we wanted to know if there was a better strategy, one more rooted in data…so we did the nerdiest thing ever. We created a graph showing the relationships between every five-letter word in the Wordle dictionary and started testing.

Could we find the optimal starting word?

Wordle is a five-letter guessing game where you’ve six opportunities to guess the secret  word. Pretty simple. But did you know there are 2,315 possible solution words in the Wordle dictionary as well as an additional 10,657 words that you can use as guesses? That leaves each player with 12,972 words to choose from each day…and 10,657 words that’ll never be the answer.

A straightforward goal with a massive number of possibilities to explore. But there are a few common strategies. Do you see yourself in any of these?

• The Gambler. This Wordle player likes to use words that have uncommon letters like X, Z, J and Q paired with common letters in hopes of getting lucky and narrowing down the number of possible words significantly.
• The Minimalist. This strategy involves choosing the most common word that comes to mind at every turn. While this approach is probably what creator Josh Wardle probably had in mind, it can take the fun out of the game.
• The Strategist. This person plays what’s known as “hard mode” where you commit to using the correct letters you already know. It prevents you from eliminating unknown letters as quickly and requires more mental work to select your next guess.

Whatever your strategy, Wordle is all about probability—the probability the word will contain or not contain a letter. Players cycle through their mental dictionary to find words that fit the pattern set so far, then try to estimate the likelihood their next guess will be the right word, or will get them closer to the answer.

But what if instead of seeing a list of possible words, you could see the pathways between those words and visualize whether you’re getting closer or farther away from the solution?

Have you ever wondered how many options there are in Wordle? This network graph illustrates every possible word—all 12,972 of them—and shows how they’re related to each other by similar letters and letter placement.

This kind of visualization is called a network graph and they help you see multiple connections in meaningful ways…so we decided to use our AI Platform to help us solve Wordle puzzles by creating network graphs illustrating the relationships in five-letter words. By visualizing and exploring the connections between words (the shared letters AND the shared placement of letters) we can understand what options are left, narrow down the possibilities, and march toward the solution with confidence.

Here’s what it looks like to play Wordle as a data scientist with an AI sidekick!

Choose a Starting Word for Wordle 

How do analytical pros choose their starting word?

We asked on LinkedIn, and 71 percent of responses said they use the same word to start every day. So we better make it a good one, right?

Most players pick a word with common letters and multiple vowels—think R, S, T, L, N and E like on Wheel of Fortune. This choice is based on logic more than a hypothesis because we have statistics for the most common letters in five-letter words. But it’s a pretty high-level strategy, so we see plenty of favorite words that are the personal preference of the player. Bias influences us all the time, even when we’re playing games!

Intelligent Exploration—the process of having AI guide us through our data—prevents bias from limiting our progress. For instance, even if cheer is your favorite word, exploring the data of the Wordle dictionary tells us starting with a double e word isn’t going to eliminate as many options as we’d like. We use our exploration to find a starting word that logically makes sense, adding AI to help us take that exploration one step further.

At first guess, most people aren’t considering the placement of each letter, or common letter combinations, because we just don’t have enough information yet. But by using AI to explore our options we find that SALET is our best starting word based on the frequency of the letters and their placement within the word. You don’t need to know the meaning of a word to use it (evidently a salet is some form chalet, which we had to look up).

As data scientists, we’re operating on two things we know to be true:

• The more you explore your data before you start, the better your first effort to find a solution will be.
• The results of your first move should be the new starting point for even deeper exploration.

And now we’re going to prove it by using SALET in a game.

Play Wordle with our Data Scientist

So you’ve got the perfect word to start with, SALET. What’s your next move? We can’t help you there, but we can show you how our Data Science Intern Max set about solving the December 12th puzzle.

Starting with SALET, Max found A and L were in the solution, but in the wrong spots. So he eliminated some possibilities, but what does that really look like? By running an algorithm to map the potential words into a network graph with our weighted edge function, this is what Max saw:

By creating a network graph, Max can see there are communities featuring common letters that can help drive his next guess. He’s also able to see the likely position of the letters representing the strongest connections. For example, the Yellow Community contains words where the third letter of the word is always an A.

Our Louvain community detection algorithm that’s built into our network graph solution picked up on some pretty cool patterns. It was able to recognize common letter positions and the most notable frequencies of letters in words and break them into different sub-graphs. Here our algorithm surfaced three different potential words for our next guess: VIOLA, PLAIN, and CLANG. Where VIOLA (the blue community above) is the most eccentric (containing the most unique combination of letters), PLAIN (in the yellow community above) has the highest weighted degree (it’s most closely related to other words because it has a lot of common letters), and CLANG is a mixture of the two (also falling in the yellow community).

VIOLA is the most eccentric option because of the V and the placement of the other letters. It could be the solution, but there are a lot of other possibilities that don’t include such a unique letter.

You can see that PLAIN has a lot of commonality with other words, illustrated here by the number of connections.

Finally, CLANG is the choice that’s the most middle of the road. Not too unique or eccentric, but with letters less common than PLAIN and therefore with fewer connections. 

That leaves us with a strategic choice: do we gamble on the guess that’s more eccentric and unique, or do we use the guess that has more common letters? Unique letters might not be in the word at all but if they are, we’ll get to the answer in a flash. A word with common letters will help us confirm our choices, but could leave us with so many possible solutions we run out of chances.

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Given there were still quite a few possible words remaining, Max chose PLAIN to narrow down the field. He probably won’t get a big win, but this choice has a higher likelihood of confirming some letters.

His next result told him that the L, A, and N were in the word, but none of them were in the right spot. Max now knows the solution must contain L, A and N but not in spots he’s already tried. Having followed the relationships in the network graph to their logical conclusion, there are only three options left in the Wordle list using our known letters in the right way:

ZONAL, LUNAR and ANNUL

You’d think ZONAL maximizes eccentricity here but no, LUNAR or ANNUL are better guesses since ZONAL is well connected to LUNAR with an A and N in the same position, and well connected to ANNUL with N and L in the same position, whereas LUNAR and ANNUL only share an N.

Max picked LUNAR and got lucky! But either way, he had enough guesses remaining to try all three of the possible solutions left. He was going to win no matter which of the three remaining words he guessed first.

Intelligent Exploration in (Wordle) Network Graphs

Let’s face it, sometimes our first Wordle guess looks like it’s going to go well (with three green letters) but then the letter combination is so common there are still tons of possibilities left, and not enough guesses to eliminate all the noise. Chances are good you’ve had a project or initiative that experienced a similar fate, where what seemed like a good idea at first glance didn’t have the impact or result you wanted.

So, what are we supposed to do?

Not trust our gut at all?

Assume we’re doomed from the start?

That area of unknown is why we use AI to explore our data before we begin and each time we guess. With a solid foundation for our strategy and monitoring of our progress, we’re able to make sure the choices we make are actually leading to progress.

We should also appreciate finding eccentricities and anomalies—the first Wordle guess that results in all gray blocks (i.e., no letters correct) actually eliminates tons of possibilities. Now you know those most popular letters, our educated hypothesis, aren’t what you’re looking for. That drives down the list of possible solutions significantly. Finding the right path or project is always the goal of Intelligent Exploration, but there’s so much value in finding out what you don’t want to do, too.

AI as a Sidekick, You as the Hero 

The Wordle solution of the day is pretty public. People don’t play because they’re dying to know what the word is—they play because they want to see how quickly they can discover the path to the solution.

Our AI platform isn’t about handing users a single, unexplained answer. Our solution is designed to explore and visualize complex data in ways data scientists and other analysts and business users can see, understand, explore even further, and share with stakeholders so you can build and execute winning strategies. You and your team are the heroes—we help you find the way.

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[To share your insights with us, please write to sghosh@martechseries.com]

The post Find the Magic Word(le) Like a Data Scientist appeared first on AiThority.

AI-powered company for neurological disorders, Combinostics has released The Dementia Differential Analysis report. This report visualizes comparisons of patient MRI biomarkers with data from patients with various types of dementia or normal cognition.

The Finland based startup is providing a complete solution from early detection and diagnosis to the ongoing management of neurological disorders. Its groundbreaking new report to assist clinicians in the detection, and differential diagnosis of dementias: the Dementia Differential Analysis report. Unlike existing solutions that compare against cognitively normal reference data only, the artificial intelligence (AI)-enabled application quantifies and evaluates patient MRI data against the distributions of key dementia-specific imaging biomarkers and reference data from approximately 2,000+ patients with a confirmed neurodegenerative disease, including frontotemporal dementia, Alzheimer’s disease, and vascular dementia.

“The Dementia Differential Analysis report is completely unique to Combinostics and will help change the paradigm of diagnosing dementias,” said Richard Hausmann, CEO of Combinostics. “Using our innovative AI technology, it is the only solution that enables true differential diagnostic support, furthering our commitment to provide clinicians with tools for reliable, evidence-based diagnostic decisions.”

Based on MRI data only and utilizing our unique cDSI™ application, the Dementia Differential Analysis report allows radiologists and neurologists to confidently and accurately interpret imaging data. The report provides a clear, concise visual summary featuring the probabilities that the patient’s characteristics match those of specific diagnostic groups and comparisons against the reference imaging biomarker data: cortical atrophy score, hippocampal atrophy score (left and right), brain tissue white matter hyperintensities, and anterior versus posterior score. Neurologists can also integrate additional clinical data, such as demographic information, cognitive testing, cerebrospinal fluid biomarkers, and more, through Combinostics cDSI for deeper data-driven diagnoses and decisions around additional testing, prognosis, and treatment eligibility.

Combinostics has also released additional tools intended to facilitate patient management as well as communication between radiologists, neurologists, patients, and their caregivers: new clinically focused cMRI™ reports* for dementia, multiple sclerosis, traumatic brain injuries, and epilepsy.

“Our team is passionate about innovating new ways to support clinicians in providing the best care for their patients with neurological disorders,” continued Richard. “With our full suite of new reports, radiologists and neurologists can be more confident in their diagnoses and have tools to clearly communicate with patients so they can understand their diagnosis.”

Learn more about the industry-first Differential Dementia Analysis report by attending “Differential Diagnosis of Dementias: MRI-only Diagnostic Trend to Enable Differentiation with Imaging Alone” at The American Society of Neuroradiology (ASNR)’s Annual Meeting in New York City.

Combinostics’ AI-powered cNeuro suite of products helps clinicians make a difference in the lives of patients with neurological disorders. By quantifying brain images and integrating patient data from multiple sources with insights from previous patients, the company’s unique software tools provide radiologists and clinicians the support they need for confident, evidence-based diagnostic and management decisions.

The post Latest AI-based Report Supports the Differential Diagnosis of Dementias appeared first on AiThority.

Artificial Intelligence (AI) is transforming how businesses analyze processes both digitally and physically, putting behavioral science at the forefront of many technology advancements today. The latest solutions can evaluate any series of interactions that take place within a company’s designated core destinations or locations — with the goal of enhancing customer experience, increasing profits and maximizing logistical efficiencies. These locations might include a diverse number of environments, such as a retail store, grocery store, shopping mall, commercial real estate office — or even a shared municipal space or public area, where spatial management is essential to streamlined operations and smooth traffic flow.

Fueled by big data, the progress in AI is transforming the economy, culture, society, and lives of individuals. It is also transforming behavioral science. Behavioral science has been around a long time and is a classic discipline charting and analyzing actions between people to predict patterns. But what if a business could get enough information about the behavior of its customers — in any number of physical locations pivotal to that organization’s success — without having to wait for a lengthy analysis?  This is where the difference between basic analytics and spatial analytics comes into play.

Basic “counting” analytics can be used for minor, more obvious changes, but other changes require a sophisticated, holistic view of the movement of people in order to produce actionable, relevant insights with little effort on the part of the end-user – or spatial analytics. This takes data results to a different level by incorporating advanced predictive analytics and Machine Learning (ML) tools and strategies.

Using spatial analytics to access this information in real-time can help businesses become not only more successful, but more effective in helping their customers.

For example, in a retail environment, having access to real-time, location-based data, managers can make changes to store floor operations to maximize sales, at any time. They can amend a display formation, better position staff to interact with customers, or adjust the flow of traffic from the entrance to the cash register. Adjustments can be made at any time during the day to respond to customer needs as they occur. This helps retailers become better equipped to support customer needs and more highly in-tuned with shopper marketing dynamics.

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With analysis in hand, companies can make any changes immediately or wait until downtime hours.  Either way, changes made based on spatial intelligence and analysis can be timed to incidents and fluctuating movements in floor traffic as they occur, an application that has never before been available to the industry. In addition to traditional brick-and-mortar retail applications, the same premise would apply, for example, to understanding the behavior of individuals as they move inside a grocery store, a commercial real estate office lobby, or inside a shopping mall. The insights and deeper knowledge into these physical movements and interactions have the potential to render game-changing financial impact.

The key element of this AI-driven intelligence revolves around the detection of ‘movement’, where an individual is recognized only as a digital dot on a visual floor plan, thus completely protecting anonymity. These movements may represent a customer in a retail establishment or a shopper in a grocery store. They might also represent the customer’s spatial interactions — not just within the physical space itself (i.e. how and where they move about a store floor) — but also with other entities (staff) moving about within the same space.

Imagine how retail management could benefit from this type of enhanced behavioral intelligence to better understand customer patterns and traffic flow — knowing these:

1. which departments attract the most customers,
2. in what direction they often travel in-store,
3. how often they wait in checkout lines,
4. in what group sizes do they enter the store.

Other critical information can be gathered such as why customers might choose to end the shopping journey altogether and leave the store. With this added knowledge, store operations can improve staffing management in checkout stands or retail merchandising teams can boost product placements according to highly trafficked areas inside a store.

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Using advanced pattern-matching capabilities that learn from data and operate in real-time, the movements of these “digital dots” are quantified both individually and in aggregate to find the hidden patterns and metrics that business owners care about. Spatial Intelligence turns simple, common location data (x, y coordinates of people over time) into the insights and events that are most valuable to business operators. Spatial Intelligence might also be used to pick out complex behaviors such as a sales associates restocking shelves or to measure various aspects of those behaviors, allowing business owners to optimize guidance, training, and real-time feedback for their associates.

This level of data-based intelligence is, at present, only possible with advanced ML protocols, Artificial Intelligence, and cutting-edge predictive analytics. In the retail context, this offers a good example of how brick-and-mortar stores can employ advanced technology to not only enhance the customer experience and better compete with online commerce, but also to directly increase their in-store growth and improve competitive performance.

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Gaining knowledge about key constituent movements provides the totality of knowledge that business managers need to make an in-location experience as satisfying as possible. In addition, the ability to translate these movements into actionable data brings behavioral science into the digital age with a new level of knowledge acquisition. Insights and deeper knowledge into these physical interactions have the potential to render game-changing financial impact.

[To share your insights with us, please write to sghosh@martechseries.com]

The post Businesses Need to Consider AI Algorithms and Spatial Intelligence to Increase Success appeared first on AiThority.

The World Health Organization (WHO) Regional Office for Africa, through the Expanded Special Project for the Elimination of Neglected Tropical Diseases (ESPEN), is making available new analytical tools in the NTD Data Portal allowing Neglected Tropical Diseases programmes and stakeholders to better track rollout and impact of interventions, and make data-driven decisions on strategies.

Big Data in healthcare signifies a massive opportunity for the global marketplace for patient monitoring systems, tropical diseases tracking,  vaccination evaluation campaigns and telemedicine developments. While we are witnessing a rapid uptick in the adoption of Big Data, AI and Machine Learning, and Deep Learning for various healthcare programs in developed and developing economies, the African region seems to be still lagging in this area. The WHO is partnering with leading big data analytics tool providers to amass relevant big data intelligence to solve immediate healthcare problems in Africa. ESPEN Data Portal is an outcome of these efforts.

What is ESPEN Data Portal: A Dedicated Data Portal for NTD Programs

ESPEN is an abbreviation for the Expanded Special Project for Elimination of Neglected Tropical Diseases. ESPEN was established in 2016, and since then, it has helped “to mobilize political, technical and financial resources to accelerate the elimination of the five most prevalent Neglected Tropical Diseases (NTDs).” ESPEN works in collaboration with the WHO Regional Office for Africa (AFRO), Member States, and NTD partners. ESPEN Data Portal is a positive step forward in the direction of seeing a disease-free Africa. The foundation lies in ESPEN’s ability to quickly collect, analyze and report district-level data in the form of maps and share these with health officials and NTD partners that help the NTD groups to reach key targeted communities within Africa.

Currently, the ESPEN Data Portal enables health ministries and stakeholders to share, and exchange subnational program data, in support of the NTD control and elimination goals.

The consolidated repository hosts data shared by health ministries through the Joint Application Package reporting system and provides a detailed ongoing picture of the status of Neglected Tropical Diseases (NTDs) programmes targeting Preventive Chemotherapy NTDs (PC-NTDs). Information is linked at the implementation unit level and can be freely accessed, enabling better tracking of progress, supporting cross-disease coordination, and facilitating comprehensive forward planning. Through the portal, users can readily view, download and validated, reliable longitudinal data and maps for planning and reporting purposes.

“It’s vital that countries do more to combat the variety of Neglected Tropical Diseases that are sadly still so prevalent across so much of Africa, causing millions of citizens severe disabilities and sometimes their lives. Access to updated and more accurate data is vital for countries to implement strategic plans that can truly help to save more lives and eliminate NTDs,” said Dr Maria Rebollo Polo, ESPEN Team Leader at WHO Regional Office for Africa.

“We encourage Neglected Tropical Disease control programmes across Africa to use this innovative tool so that their activities and investments result in real impact and can be coordinated in the most efficient way, making the most of often scarce resources,” Dr Polo said.

New Analytical Tools for Guiding NTD Country Programs

Evidence-based decision making for Neglected Tropical Diseases helps drive progress. The ESPEN Data Portal has expanded to include a comprehensive suite of data dashboards designed to help Neglected Tropical Diseases programmes better track roll-out and impact of interventions and make data-driven decisions on future strategies.

The new ESPEN Progress and Forecast dashboards allow users to explore key statistics and analytics, graphics and maps, at both sub-national and national level. They outline current endemicity and progress on mass drug administration (MDA) interventions to date for each of the PC-NTDs, together with future treatment and impact assessment needs over the next ten years. The dashboards have been purpose-designed to support national programmes to readily access and use their data, make better-informed decisions, and distribute resources more efficiently. Disease-specific dashboards can be accessed through country pages – simply visit your country page, and select the disease you’re interested in.

Making Better Use of Our Data

To complement the existing suite of maps and datasets, WHO Regional Office for Africa has now developed interactive dashboards detailing both current progress and projections for the next 10 years at the level of implementation. Using historical data compiled under the ESPEN data repository, we have forecasted when MDA interventions will be needed, what type of MDA strategy should be implemented (considering co-endemicity), and when impact assessment should be conducted, to achieve the goals established by the new 2021-2030 Roadmap for the Elimination of Neglected Tropical Diseases. These resources can greatly support the completion of both National Neglected Tropical Diseases Masterplans, and Annual Work Plans.

These are new tools and, as such, may still be improved based on the feedback received from data portal users. We hugely value feedback from Neglected Tropical Diseases country programmes and partners on these new resources and the portal in general.

ESPEN is a WHO project created in the spirit of a public-private partnership with the goal of accelerating the elimination of five PC-NTDs in Africa. Since its launch in May 2016, ESPEN works with domestic and international partners to leverage US$ 17.8 billion in drug donations from pharmaceutical companies to expand coverage and access to treatments, strengthen health systems and provide Universal health coverage of interventions against PC-NTDs in Africa until we reach final elimination of these devastating diseases.

The post WHO Announces Latest Big Data Analytics Tool for Healthcare – ESPEN Data Portal appeared first on AiThority.

DeepMind has managed to solve decades-old problem with its machine learning model which opens up new avenue for AI-based predictions on protein sequencing and advanced research.

DeepMind’s ambitious AI-based AlphaFold system has managed to solve a complex cellular biology challenge that puzzled genome researchers for over 50 years. The DeepMind team published its first-ever AlphaFold Database. This Database provides open source access to protein structure predictions for 21 organisms, including humans. The new database would help researchers accelerate their work in the field of molecular biology and pharma research. Mapping of protein structures using 3D models built on DeepMind’s cutting-edge AI technology could provide massive trove of information related to human proteome and much more.

The 50-year Old Protein Fold Challenge

Proteins are the building blocks of life. There are two main principles driving the nature of protein once can synthesize in a lab—amino acids and the 3d structure. While we know more or less how amino acids work in chains, the 3D structuring remained a big puzzle for years. Researchers were unable to solve a unique challenge in how proteins fold in shapes. This problem is famously called as “Protein Folding Problem’ or PFP. Scientists at DeepMind solved the PFP puzzle by advancing their AI model, driving it through AlphaFold project.

What is DeepMind’s AlphaFold Project?

AlphaFold is DeepMind’s AI system used as a forecasting tool to map the 3d structure of proteins based on amino acid sequencing. DeepMind co-partnered with EMBL’s European Bioinformatics Institute (EMBL-EBI)to develop the AlphaFold Database (DB). The information available in AlphaFold can be freely accessed and modified for the benefit of scientific community engaged in the protein sequencing and genetic mapping research.

Organizers of Critical Assessment of protein Structure Prediction (CASP) recognized DeepMind AI system AlphaFold as a solution to the grand PFP challenge.

AlphaFold2 demonstrates the strong correlation between computational work in the field of biology and advanced molecular research. This could further assist in improving accuracy of predictions used to understand the nature and structure of important protein classes, including membrane proteins. By bringing together biology, physics and machine learning, DeepMind intends to positively influence the penetration of AI and computing for advanced sciences like drug design and environmental sustainability.

The post DeepMind’s AlphaFold2 Solves 50-year Old Protein Fold Challenge appeared first on AiThority.

Patent empowers users with seamless voice and video calling for improved communications

Fuze, the leading cloud-based communications provider for the modern global enterprise, announced that it has been awarded a new patent for selecting routes through a network, enabling seamless voice and video communication between Fuze users to deliver an optimized customer experience.

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The U.S. Patent and Trademark Office (USPTO) issued Fuze U.S. Patent No. 10554720B1 on February 4, 2020. The invention intelligently selects the best routes or paths for communication between computer systems through a network based on capacity and the source and destination of the call. This process helps to avoid excessive network traffic impacting the quality of the call or resulting in a dropped call, enabling reliable communication over the Fuze platform.

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“On any network, problems can easily arise during the transmission of calls, leading to low-quality calls and network failures,” said Rob Scudiere, president and chief operating officer at Fuze. “With the ability to select optimal network routes, this patent empowers our users with the reliable and seamless communication workflows required in our increasingly distributed global workforce.”

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The post Fuze Enhances Enterprise Communications With New Patent for Selecting the Most Reliable Network Routes appeared first on AiThority.

With Network Traffic Surging and New Visibility Gaps, Kentik Is “the Right Company at Exactly the Right Time”

Kentik, provider of the leading network intelligence platform trusted by digital enterprises and service providers, announced $23.5 million in growth funding led by Vistara Capital Partners. Existing investors August Capital, Third Point Ventures, DCVC, and Tahoma Ventures also participated in the round, which combines equity and growth debt. To date, Kentik has raised $61.7 million in total funding.

“Kentik is the right company at exactly the right time. The company already keeps industry-leading enterprises and the largest service providers across the globe operating with the fastest network intelligence and business insights for the most optimal digital experiences,” said Randy Garg, Founder and Managing Partner of Vistara Capital Partners. “Our investment in Kentik demonstrates our confidence in the company as a market leader with a position for the next phase of growth, bringing new products to even more markets.”

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Always-on networks are essential, now more than ever, and Kentik is the solution businesses trust to keep the digital world connected. Leading SaaS companies that fuel the new normals of remote work and increased internet presence depend on Kentik as the only solution to close the network visibility gap and unlock real-time network intelligence. Companies like IBM (NYSE: IBM), Zoom (NASDAQ: ZM), Dropbox (NASDAQ: DBX), eBay (NASDAQ: EBAY), Cisco (NASDAQ: CSCO), and GoDaddy (NYSE: GDDY) turn to the Kentik Network Intelligence Platform for instant analytics and insights across cloud and hybrid environments to make informed network and business decisions.

“At Dropbox, our customers depend on us to keep the global workforce connected, and we’ve built a robust infrastructure to ensure the reliability of our services and support,” said Dzmitry Markovich, Senior Director of Engineering at Dropbox. “As a great partner with a strong roadmap, Kentik provides the real-time visibility, traffic management, and network intelligence we need to deliver a great experience for our customers.”

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“Over the past few months, many of our customers have experienced a 200% to 500% increase in traffic growth on their networks. With recent traffic growth, we now have real-time visibility into over 1 trillion traffic measurements per day across billions of users, and see every network connected to the internet, and every cloud and SaaS provider,” said Avi Freedman, co-founder and CEO of Kentik. “The Kentik platform was built to scale and provide real-time network intelligence, even in unprecedented times, to close visibility gaps and enable businesses to remain always-on.”

“With a tremendous customer base, an essential platform, a roadmap that includes new products, and solid investor backing, Kentik is well-positioned for growth,” added Freedman.

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The post Kentik Raises $23.5 Million in Growth Funding appeared first on AiThority.

New Trade Group for Business Development and Strategic Partnership Professionals Brings Networking, Training, and Professional Development Opportunities to a Global Audience

BIZDEV: The International Association for Business Development and Strategic Partnerships the trade organization with teamwork and collaboration at its core launches, bringing networking, training, and professional development opportunities to industry pros around the globe. Representing business development and strategic partnership professionals worldwide, the association aims to help members create value and maximize commercial opportunities by providing the research, online/live events, and education programs that executives need to successfully work together and create cutting-edge business strategies and solutions.

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A dynamic industry that’s home to over 37 million professionals, and more than 296,000 job openings in the last month alone, the field of business development is com.mitted to creating winning strategic opportunities for organizations of every type and size around the world

“Business development pros continue to help organizations in every field drive growth, accelerate innovation, and explore promising new opportunities on an unprecedented scale,” says Scott Steinberg, CEO and President of BIZDEV™. “We’re honored to support industry professionals in every sector in their mission to cultivate and nurture successful relationships by providing them with the education, training, and research that they need to stay one step ahead of emerging trends – and one step ahead of the curve.”

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Today’s leading voice for market research, networking events, and training programs, membership – which will temporarily be free for 60 days as a special, limited-time offer for businesses who have been impacted by the spread of coronavirus – is available at www.BizDevAssociation.com and includes exclusive perks such as access to:

–       Market Research and Business Intelligence

–       Articles, Videos, and Learning Resources

–       Exclusive Live and Online Events

–       Free Webcasts and Training Guides

–       Discounts on Conferences, Seminars, and Certification Workshops

Open to professionals in all fields, and in every nation, sample areas of practice the association works within include:

–       Entertainment and Media

–       Advertising, Marketing, and Public Relations

–       Consumer Products and B2B Solutions

–       Technology, IT, and Cyber Security

–       Healthcare, Wellness, and Life Sciences

–       Movies, Music, TV, Video Games

–       Retail and Distribution

“Business development and strategic partnership pros are continually asked to exercise a wide range of skills and talents from sales and strategic planning to customer service, market research, and brand development,” explains Steinberg. “The first association of its kind that’s designed to help members in their efforts to turbo-charge growth, fearlessly innovate, and promote positive change at all levels, we’re proud to provide them with the tools that they need to build both lasting relationships and tomorrow’s most forward-thinking solutions.”

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The post BIZDEV: The IA for Business Development and Strategic Partnerships Promotes Cross-Company Collaboration & Teamwork Worldwide appeared first on AiThority.

Nearly all narrow enough tasks are better solved by an AI today than by us humans.

Know My Company

Tell us about your journey into the intelligent tech industry of AI and Machine Learning. What galvanized you to join Blackwood Seven?

I had been working for quite some time in the media space with advanced econometric modeling and saw first hand how difficult it is to get from insights to action. Blackwood Seven is an idea that allowed us to disrupt an existing ecosystem by using Machine Learning as an integrated part of media allocation and budgeting.

What does it take to start and succeed in a Deep Learning Tech startup ecosystem?

As with anything, you need to make sure that you’re actually addressing a need and not only developing tech for the sake of tech. There are a lot of cool software and applications developed where the purpose is not really clear and most people are not ready to pay for it.

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How do you prepare for an AI-driven world as a business leader?

I spend a lot of time evangelizing AI and it’s huge potential but also the current weaknesses that we all need to be wary of. There are jobs disappearing from the job market within the near future due to the advancement of AI. However, many new jobs will appear, and I for one try to map those jobs out and surround myself with people who share my passion for AI.

How is the role of Chief AI Officer different from that of a Data Officer/ Data Scientist?

To be honest, I’m not sure the titles are as important as the work you do. That being said, there are of course differences. You can be an awesome Data Officer or even a Data Scientist without knowing much about AI. I see the three roles as complementary and necessary. Using the AI Officer title in your organization also indicates that you have taken the opportunity seriously and are working actively with it in your strategy.

How is AI/ML unlocking the capabilities in Human Intelligence?

Well, AI can do many things, but one of them is for sure to augment our current senses. Nearly all narrow enough tasks are better solved by an AI today than by us humans. When it comes to more holistic planning and longer-term development and analysis the human mind is still vastly superior.

What are the foundational tenets of your AI/ML missions? How could business and society benefit from your initiatives?

For me personally, I’m driven by the mission to bring AI to everyone. I believe that AI will be the greatest thing to ever happen in the history of mankind and that it will allow us to scale our knowledge and technologies beyond the realm of what we currently believe is possible. Its application will also not be limited to verticals or types of businesses. I see myself as an enabler, creating the technology that allows society and businesses to include AI in their everyday decision making processes.

Which AI technologies are most likely to impact the Marketing and Sales businesses?

We will see a surge coming surrounding the Bayesian way of modeling. Especially, since it allows an AI to inform its users about the uncertainty regarding each new prediction.

Specific topics that will be addressed within a short period of time is automated, individual and optimal pricing along with a higher rate of specificity with respect to the consumer. AI is also moving in on the creative space today.

How do you see IoT, Robotics and Cloud Computing – all coming together with AI/ML to enhance ROI in traditional businesses and the global economy?

It’s hard for me to imagine any product, platform or service not containing AI within the next 5 years. I fundamentally believe that AI will be as core to these concepts as the ability to connect to the internet. Further, the world is decentralizing and it’s not unthinkable that it will be an AI designing the final communication protocol that is to be used.

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What are your top predictions and must-watch AI/ML-related technologies for 2018-2022? How much of these technologies would be influenced by socioeconomic trends?

Basically, anything addressing the current shortcomings of AI today will be interesting to follow. Especially interesting technologies in this space will be General Adversarial Networks, Bayesian methodologies, and semi-supervised learning. I think we’ve reached a pivoting point in AI research and development, which means that I for one do not believe that socio-economic trends will affect it much. It’s not like the space program, which requires enormous funds since anyone with 10,000 USD in their account can do some serious AI research in their basement!

Tell us about your AI and Deep Learning research programs?

At Blackwood AI Research, we are developing an AI engine manifesting itself as an in-silico data scientist capable of automatically building complex Bayesian Hierarchical Probabilistic Graphs, which is used for Marketing Mix Modeling applications.

What’s the “Good, the Bad, and the Ugly’ about AI and how do you prepare for these situations at Blackwood Seven A/S?

The good is that what we’ve built actually works and provides a tonne of value to our customers.

The bad part is that we require structured data to do it. This data can sometimes be tricky to consolidate internally in an organization. The state of data in general in the world is not AI ready.

The ugly thing about AI today is that most solutions out there are based on a flawed concept and an extrapolation of data to new situations. At Blackwood, we deal with this by quantifying the uncertainty in all scenarios. This allows our AI to always produce the most optimal risk-adjusted recommendation.

Do you think “Weaponization of AI/ Intelligence” is the biggest threat to mankind now?

Sadly, I do believe that the military will be one of the first entities to put AI to the test. Replacing infantry is something that is not as far away as we might think. With the advancements of motor control and sensory inputs combined with tracking abilities, there’s not much in the way of running “pilots”.

The Crystal Gaze

What AI and Machine Learning start-ups and labs are you keenly following?

DeepMind, Vicarious, OpenAI to name a few.

What technologies within AI and computing are you interested in?

I’m primarily interested in learning representation and the definition of intelligence. As such anything Bayesian combined with domain knowledge and reinforcement learning is of great interest to me. Computationally I’m interested in MCMC sampling or other improved ways of exploring the vast parameter landscapes of our models.

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As an AI leader, what industries you think would be fastest to adopting AI/ML with smooth efficiency? What are the new emerging markets for AI technology markets?

I think retailers and automotive will be heavy users in many markets. With respect to investing in and pursuing the development of AI, I very much see all of Europe as an emerging market. Especially, compared to China and the USA!

What’s your smartest work-related shortcut or productivity hack?

Stop PowerPoint-ing, use a software that does the formatting and layout for you. Focus on the content!

Tag the one person in the industry whose answers to these questions you would love to read:

Geoff Hinton

Thank you, Dr. Michael! That was fun and hope to see you back on AiThority soon.

• About Dr. Michael
• About Blackwood Seven

About Dr. Michael

Dr. Michael is a driven artificial intelligence professional and solutions architect with vast experience in statistical modeling, machine learning and data analysis in the marketing and business intelligence domain. Specifically interested in predictive modeling and analytics applied to domain-specific problems.

Primary goal is to continue introducing state of the art statistical modeling in business intelligence workflows.

About Blackwood Seven

Blackwood Seven was founded in Copenhagen in 2013 by a group of former CEOs from marketing and IT, who wanted to revolutionize marketing by fully embracing technology. Since then, we have been growing fast with offices in New York, Los Angeles, Munich and Barcelona.

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