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Like most nebulous technologies marketed as the cure-all for the enterprise in the 21st century, artificial intelligence--and more specifically anyone tasked with selling it--promises a lot. But there are some major obstacles to adoption for both the public and private sector, and understanding them is key to understanding the limits and potential of AI technologies as well as the risks inherent in the Wild West of enterprise solutions.

Consulting firm Booz Allen Hamilton has helped the US Army use AI for predictive maintenance and the FDA to better understand and combat the opioid crisis, so it knows a thing or two about getting large, risk-averse organizations behind meaningful AI deployments.

For insights on where AI still stumbles, as well the hurdles it will have to clear, I reached out to Booz Allen'sKathleen Featheringham, Director of AI Strategy & Training. She identified the five greatest barriers to AI adoption, which apply equally to public and private sector organizations.

Note: The below answers to interview questions have been rearrangedand formatted slightly to obtain listicle perfection. All language is Kathleen's, with thanks for her keen insights.

AI governance or the lack thereof. As with any powerful technology, AI requires structure in its implementation, which should govern its capabilities, and ethical principles.

It's important to remember that AI solutions are built by imperfect humans. We've seen examples of models that unintentionally generate discriminatory outcomes because the underlying data was skewed towards a particular segment of the population. Whether they resulted from bias in the dataset (e.g., exclusion or sample bias) or from humans' unconscious biases, these outcomes rightly erode trust in the technology and slow adoption.

So how do we fix it?

We must balance freedom, ethics and privacy with efficiency and other benefits AI makes possible.This foundation for AI requires that people at all levels of an organization understand their role in building a governance structure. A strong governance system includes a set of ethical design and development principles that are regularly reviewed, creating a "feedback loop."

It's important to consider these three points when developing a governance framework for AI: 1. Prioritize ethics early. 2. Build robust, transparent, and explainable systems that clearly yield an audit trail with the understanding as the models learn these can adjust. 3. Ensure measured, monitored roll-outs with robust governance and oversight, guided by clearly document processes.

Although AI could be the most transformative technological development of our lifetime, a methodical approach to implementation and adoption is critical. This starts with readying the organization from a cultural standpoint, enabling adoption through effective education on the technology (and therefore trust in it) and offering the necessary technical training.

There has been too much concentration on one-and-done tool trainings which hamper the development of the next generation of data engineers, of which there is a critical shortage currently. Continual education and training over years is needed to evolve their tradecraft/skills and speed adoption.

So how do we fix it?

Successful and ethical adoption of AI relies on people who understand and are empowered to put this technology to work. This means building a diverse and AI-knowledgeable workforce, creating opportunities for upskilling and learning across disciplines.

It is equally important to communicate the organization's objectives clearly while giving employees a voice in how AI will affect the workplace.

The data and systems operated by AI must be protected from both accidental and malicious interference. There are bad actors who attempt to change AI outcomes by "poisoning" underlying data. A familiar example is a few pieces of tap that trick an autonomous car into seeing a speed limit road sign as a "Stop" sign. This and privacy are very real concerns given AI must be entrusted with a certain amount of autonomy to perform its tasks.

AI is still vulnerable to adversarial attacks where it can be "tricked" and its analytical capabilities put to nefarious use. Given the vast amounts of data AI's needs to perform, protecting that data becomes of paramount importance. And since AI's decision-making process is still largely a black box, this is a vulnerability that causes great concern.

The solution? It's all about transparency ...

Because AI is still evolving from its nascency, different end users may have wildly different understandings about its current abilities, best uses and even how it works. This contributes to a blackbox around AI decision-making. To gain transparency into how an AI model reaches end results, it is necessary to build measures that document the AI's decision-making process. In AI's early stage, transparency is crucial to establishing trust and adoption.

While AI's promise is exciting, its adoption is slowed by historical fear of new technologies. As a result, organizations become overwhelmed and don't know where to start. When pressured by senior leadership, and driven by guesswork rather than priorities, organizations rush to enterprise AI implementation that creates more problems.

Which leads us to ...

AI often relies on large volumes of historical data and sophisticated mathematics. Before an AI project can be implemented, organizations must achieve a certain level of data and infrastructure readiness. Common barriers include data shortcomings and disparate data sources, lack of technological infrastructure, testing inefficiencies and collaboration issues.

AI needs a strong infrastructure as its foundation, including high-performing and scalable computing systems, high volume storage systems, and GPU architecture. The process of effectively developing, deploying, and monitoring models in production environments is time-consuming and many organizations simply do not know how to operationalize their data platforms at enterprise scale. Furthermore, the data that AI utilizes must be significantly scrubbed, but organizations have not invested properly in doing so, which limits the insights AI and predictive analytics can provide. Failure to invest in and establish a strong infrastructure is responsible for much of the estimated 90 percent of AI models that are never put into production.

How to do it right?

Organizations that understand their organizational mission, data and infrastructure, and ethical needs and articulate that in a robust AI strategy can hit the ground running. During design and development, organizations must leverage strategies like human-centered design to ensure end users' needs inform system design. Strong data strategies include standardized methods for labeling, validating, cleaning, and organizing data across an enterprise. Choosing an open source platform solution will yield crucial insights into the health and lineage of data and can remove organizational data siloes and allow for a better, enterprise-wide approach to data management. Finally, investment in the infrastructure (e.g., cloud, GPUs) needed to support AI solutions is a critical foundational step as computing power is essential to enabling AI.

Ultimately, spending the time upfront to organize, prioritize, and execute against mission, data and infrastructure, and ethical needs is the best way to position organizations for long-term success.

We have seen positive signs that the private sector is ready to embrace AI and Advanced Analyticsand in many cases already has. As both the public and private sector navigate expected challenge in this journey, we're hopeful as history shows us that technology transformation is more a question or when than if. And, AI has attracted many leaders both in technology and adjacent fields, creating a robust and necessary discussion about how we build and deploy AI.Additionally, it's encouraging to see that there are many in industry have a perspective of 'Don't Go It Alone,' developing important partnerships that bring all the pieces together. Booz Allen, for example, has been working to demystify AI for the public sector, working together to bring NVIDIA'sdeep learning trainingto the Federal sector. Together, we've trained people from more than 15 government organizations within just the last year.

Ultimately, we are excited about the AI-powered possibilities that lay ahead. AI has already plays an important role in combating cybercrime and it helped speed our global response to the COVID-19 pandemic. It is important that we remember, however, that AI is ultimately an enabler that will help humans tackle seemingly complex challenges.

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5 reasons AI isn't being adopted at your organization (and how to fix it) - ZDNet

Snorkel AI cofounders (L to R): Alex Ratner (CEO), Chris R (Board Member), Paroma Varma (Head of ... [+] Solutions), Braden Hancock (Head of Technology), and Henry Ehrenberg (Head of Engineering)

In 2014, computer science PhD candidate Alex Ratner and a team of fellow Stanford PhDs, advised by associate professor and MacArthur Fellow Chris R, were working on a research project at the universitys prominent AI Lab. The main issue they focused on was companies not being able to deploy AI as widely and effectively as they wanted to, due to the costly and time-consuming manual labeling of the data that machine learning models learn from.

Like many academic projects, it was meant to be just an afternoon of messing around and a whiteboard with some math, Ratner says. Soon it turned out that this question that we had started with, of what if we changed the paradigm from labeling by hand to labeling programmatically, was quite interesting to a lot of people.

After spending five years developing the product and deploying it at organizations like Google, Apple, Intel, and the departments of Justice and Defense, in 2019 the research team spun out of the AI Lab and created a company called Snorkel AI.

Today, the enterprise came out of stealth mode announcing that it had raised a total of $15 million (combined seed and Series A rounds), from investors like Greylock Partners, GV, and In-Q-Tel.

We were motivated by this mission of not just publishing more papers on some of these fun algorithmic or theoretical ideas, but actually making AI more broadly practical with a new end to end platform that focuses centrally on the problem of data labeling, Ratner, who serves as the companys CEO, says.

Snorkel AI's platform

The companys flagship product is the end-to-end Machine Learning platform called Snorkel Flow, which allows for AI applications to be deployed programmatically at much faster rates.

Snorkel Flow would serve as a replacement of armies of human labelers which at the moment do it by hand. An example of those manual processes include training AI applications to assist a radiologist in triaging chest X-rays. The radiologist would have to sit through a ton of images labeling which ones are emergency and which ones arent to teach the AI algorithm. Another example is a bank wanting AI to classify, sort and pull information out of someones loan portfolio. The companies would need to have their legal team check and label thousands of documents by hand every single time they want to change something.

Our key focus has been on sectors where labeling data by hand is not just a slower or more expensive option, but is often just a non-starter, Ratner says.

According to Ratner, this is usually due to one or more of three factors: the data is private so companies cant outsource it to get it labeled outside of the organization, the data requires in-demand experts (doctors or legal analysts), and the data changes frequently so companies find themselves labeling and relabeling all the time.

Snorkel AIs platform enables a programmatic approach so that instead of labeling one document at a time, the user can write a function (for example if they see the word employment in the header, they can label it as an employment contract).

The advantage is that writing a dozen or two dozen of these labeling functions to label your AI solution is orders of magnitude faster than labeling documents by hand, Ratner says.

Snorkel AI label

The Palo Alto-based Snorkel AI which counts around two dozen employees, raised a $3 million seed round in June of last year, and a $12 million Series A in October. The companys current customers include two top US banks, government agencies and other Fortune 500 companies.

Saam Motamedi, an Under 30 honoree and a general partner at Greylock Partners which co-led the seed round and led the Series A, says that Greylock immediately wanted to partner with the Snorkel AI cofounders given the caliber of the team, the traction of the open source Snorkel project and the power of the paradigm shift they are pioneering around this data-centric approach.

Customers have been able to go from what took months to deploy AI applications to now being able to deploy it in hours because they can programmatically manage the data, Motamedi says.

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Meet The Stanford AI Lab Alums That Raised $15 Million To Optimize Machine Learning - Forbes

The energy grid is poised for major change through such technologies as AI and 5G. But with advancements come new cybersecurity challenges.

Key takeaways from this article:

For the energy industry, securing the grid is mission-critical.

Increasingly, too, securing devices that lie beyond the centralized grid at the edge, so to speak is also critical as well as a moving target. Zero-trust cybersecurity, 5G connectivity and machine learning, though, may ultimately help this smart grid, as this connected energy grid is known, become more resilient in the face of attacks.

While the shift toward sustainable energy could help secure a better future for the planet and reduce carbon footprint, the smart grid fueled by connected things, microgrids and so on creates two-way, risky data flows that add complexity to an already antiquated energy grid.

Smart grid technologies can balance peak demand, flatten the load curve and make energy generation sources more efficient, said Brian Crow, Sensus vice president of analytic solutions, in a recent article on the role of IoT in utilities.

Malicious attackers can exploit these two-way flows.

These devices at the edge have the potential to impact grid reliability, said Christine Hertzog, principal technical leader at Electric Power Research Institute. Malicious actors can target the grid and have the ability to change the load in dramatic ways, she said, and you could then see some issues with grid reliability.

Distributed Energy, Smart Grids Accelerate

New energy sources and distribution methods including solar panels, generators and microgrids show promise in curbing climate change and helping consumers take greater control of energy consumption during peak usage times.

Smart grid technologies decentralize energy delivery, enabling people to quickly connect to and disconnect from the larger grid and generate and deliver electricity locally. Unlike todays massive, centralized grid, an attack or disruption of a microgrid, for example, doesnt affect the entire system. Thats important for areas like California where wildfires can prompt spontaneous grid shutdowns.

But smart grids also create erratic demand on the larger grid and present two-way traffic to that grid, posing security risks. And these risks are amplified by aging energy grid infrastructure.

Decentralized energy production components of which are used in smart grids is growing. The International Energy Agency expects that renewable energy capacity will increase by 50% through 2024, with solar photovoltaics and onshore wind making up the lions share of that increase.

The whole world is moving more toward that paradigm of relying on on-site power whether its solar, a backup generator or another device, said Peter Asmus, principal research analyst of Navigant Research.

The world is shifting from large centralized resources to look more like telecom, Asmus said. He noted that while some deployments have slowed down because of coronavirus, he anticipates a greater acceleration of decentralized energy sources over the next couple of years.

Grid Edge Brings Complexity to Already Antiquated Energy Grid

The traditional energy grid itself lags these modern developments. According to the U.S. Energy Department, 70% of the grids transmission lines and power transformers are more than 25 years old, and the average age of power plants is more than 30 years old. Parts of the U.S. grid network are more than a century old.

Technologies such as the Internet of Things (IoT) devices, edge computing architecture and machine learning will modernize the grid. Examples include IoT-enabled backup generators that provide additional power to a home, electric vehicle charging stations or connected thermostats. These kinds of technologies are rapidly becoming extensions of the traditional grid.

According to the Internet of Things in Energy Market report, the global market for IoT in energy is expected to grow from $20.2 billion in 2020 to $35.2 billion by 2025, with a compound annual growth rate of 11.8% during the forecast period.

Just as connected devices are part of this equation, edge architecture is as well.

Edge computing architecture brings compute and data closer to the devices and users that need them to improve response times and reduce bandwidth needs. Myriad devices have emerged and reside at the edge rather than in the cloud, which requires a round trip from device to the cloud and back, increasing bandwidth requirements, reducing response time and potentially posing securing risks.

This is what we would call grid edge, and its a paradigm shift, Hertzog said. We used to consider cybersecurity like the fort concept: You have a perimeter. But when youre talking about the edge of the grid and cloud-based apps, youre blowing up that concept, she said.

Grid edge architecture adds risk and complexity to the grid. Edge devices may not have been patched and updated frequently, may have less vigorous authentication protocols applied, may share a network with other key IT systems and become a target for infiltration, or they may house poorly written code that is easy to penetrate and thus a target for malicious attackers.

These kinds of security risks have been amplified as utilities turn to IoT for better grid management and as consumers take advantage of devices at the edge, such as connected energy meters and home-charging stations for electric cars. As a result, security breaches can now be bidirectional, enabling grids to be penetrated not only via their own networks but also via consumer devices connected to the grid.

To combat security issues, businesses are implementing private networks for IoT. In a recent Omdia survey on IoT adoption, 97% of respondents said that they had considered or are using private networks for IoT deployments to bolster security.

AI, Zero-Trust Cybersecurity

A potential antidote to these risks is the emergence of machine learning and AI-enabled tools to aid IT pros. Machine learning tools can identify threats among the vast number of alerts that IT pros may receive. AI-enabled cybersecurity tools are becoming key to edge security, because humans simply cant keep up with all the information.

On a massive scale, that data starts to go beyond what the human brain capacity is able to do, Hertzog said. Were getting so much additional information through new tools and capability, but the ability to assimilate and make sense of that is going to be a big challenge.

Companies such as National Grid Partners have enlisted AI for cybersecurity monitoring and anticipate using automation for other tasks, such as predictive maintenance and customer service.

Hertzog said that AI is critical for validating identity at the edge, which requires a zero-trust cybersecurity strategy. The underlying principle of zero trust is to never trust and always verify.

Hertzog noted that this approach to cybersecurity requires intelligence at the edge to achieve that identity authentication. We need distributed intelligence to deliver that zero trust down to the granular level, she said. AI would be involved in looking at all the activity and seeing if there were any anomalies.

We can take this data and inform our decision-making, Hertzog said. She emphasized that true AI for this use case may be in the distance, but automated monitoring is already in place.

Hertzog also noted, however, that automation in decision-making can only take place if the data derived is accurate, clean and ready to use.

Garbage in, garbage out, Hertzog said. Hertzog noted that poor data quality is compelling reasons for utilities to put the work into data management for cybersecurity. Studies indicate that about 80% of the time spent on a project involving AI is getting that data into the right format just getting it ready to be used for AI.

AI will also require greater speed and network slicing which allows networks to be partitioned to provide different levels of access to the grid to enable granular security policy setting. Such fine-grained policies are needed to protect these distributed networks.

Hertzog and others have noted that corollary technologies such as 5G connectivity, the new wireless standard, could bolster zero-trust security by providing the network bandwidth to enable the speed and data intensiveness needed for intelligent activity at the edge.

5G is a game changer, Hertzog said. It will enable this concept of slicing networks and the ability to define security policies more granularly. That has some ramifications for zero trust.

At the same time, Hertzog said, while 5G will bolster smart grid security, the infrastructure required isnt coming tomorrow. It will take a decade to roll out, she said.

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Smart Grid Security Will Get Boost from AI and 5G - IoT World Today

Artificial intelligence (AI) technology is rapidly changing the workforce and Shiyan Jiang, Ph.D., assistant professor of learning design and technology at the NC State College of Education, is helping high school students explore and understand the technology through her work on a new grant-funded project.

Jiang will serve as the co-principal investigator on the three-year, $310,581 Narrative Modeling with StoryQ: Integrating Mathematics, Language Arts, and Computing to Create Pathways to Artificial Intelligence Careers project, which is funded by the National Science Foundation and led by the Concord Consortium.

High school is a very important time for students to develop career interests in STEM and information and communication technology (ICT) fields. We want to seize this important stage to plant a seed in their mind about what AI is, how AI will impact the world that we live in and also what kind of career choices they can make, Jiang said.

Jiang will work with a multidisciplinary team to develop StoryQ, a web-based text mining and narrative modeling platform, which will be designed in collaboration with the cohort teachers who will then utilize the platform in their classrooms. The project, which will be implemented at West Johnston High School, will cross disciplines, integrating language arts, mathematics and computer science educators.

StoryQ will consist of a 12-lesson curriculum that will teach students about artificial intelligence by using the technology to classify stories that they have written.

Students will begin using an AI model developed by the project team to classify elements of their texts, such as whether or not a character is a hero or villain, and then make educated guesses as to why the AI made those classifications. As the lessons progress, students will learn the process of how the model makes decisions based on text evidence and will ultimately rewrite their stories to change the predictions made by the AI model. This will help students develop an understanding about how human judgment or interpretation can influence artificial intelligence to make different decisions.

Jiang said that the blend of technology and writing will help students not only understand how artificial intelligence works, but become stronger writers as they pay attention to the ways in which their choice of sentence structure and vocabulary can change the AI models interpretation of the text.

We want to highlight that humans play a very important role in developing AI technology, Jiang said. In the process, we expect there will be a lot of rich discussions around vocabulary, the habits of characters and cultural awareness in the writings.

Jiang said that its important to give students an opportunity to bring their personal knowledge and culture into the writings that will be analyzed by the AI model. Having ownership of the text will allow the students to conduct better analysis of the ways artificial intelligence correctly and incorrectly classifies information from their stories. In addition, the use of personal writings allow the students to develop a close STEM identity by establishing a connection between themselves and the work they create through StoryQ.

The project will specifically target students who have been historically underrepresented in STEM, which Jiang hopes will ultimately bring more diversity into the field. Through her previous work in natural language processing, a subfield of AI, Jiang said there is a lack of diversity in this field, which means that the AI model would heavily reflect a particular perspective.

AI technology always has the developers intention. If you evoke more perspectives and diverse backgrounds, it will make the AI better. Including more people from historically underrepresented groups will make the AI field better, Jiang said. We want to empower students to see that all these opportunities are equal to them and they can belong and succeed in this field.

Jiang said the project team chose to focus on bringing AI into high school classrooms because its a field that is currently seeing high workforce demand, but they hope that the framework developed through this grant could eventually be applied to other STEM disciplines to engage a diverse group of students in technology intensive fields.

Although she understands that not all students who participate in StoryQ will go on to become AI professionals, Jiang says it is important that they have a basic understanding of how artificial intelligence works as they enter a workforce where the technology is becoming increasingly more common.

At a minimum, those kids should have a fundamental knowledge about how intelligence in computers is created, what the biases in AI are and how we can reduce biases to create AI for social good, she said. This project will help students join the public discourse about AI, but also help them develop career interests or maybe even inspire them to do future work in a career that will be empowered by AI.

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Assistant Professor Shiyan Jiang Helps High School Students Understand Artificial Intelligence Through Work on Grant-funded Project - NC State College...

Meet Florence, WHO's first virtual health worker, designed to help the world's 1.3 billion tobacco users quit.She uses artificial intelligence to dispel myths around COVID-19 and smoking and helps people develop a personalized plan to quit tobacco.

Users can rely on Florenceas a trusted source of information to achieve their quit goals. She can also help recommend tobacco users to further national toll-free quit lines or apps that can help you with your quit journey. You can interact with her via video or text.

Around 60% of tobacco users worldwide say they want to quit, only 30% of them have access to the tools they need, like counsellors, to take action.

Quitting smoking is more important than ever as evidence reveals that smokers are more vulnerable than non-smokers to developing a severe case of COVID-19.

Florence develops your quit plan using the 'STAR' method:

Set a quit date.It is important to set a quit date as soon as possible. Giving yourself a short period to quit will keep you focused and motivated to achieve your goal.

Tell your friends, family, and coworkers.It is important to share your goal to quit with those you interact frequently.

Anticipate challenges to the upcoming quit attempt.Particularly during the critical first few weeks, which arethe hardest due to potential nicotine withdrawal symptoms as well as the obstacles presented by breaking any habit.

Remove tobacco products from your environment.Its best to rid yourself of such temptations by making a smoke free house, avoiding smoking areas, and asking your peers to not smoke around you.

Florence was created with technology developed by San Francisco and New Zealand based Digital People company Soul Machines, with support from Amazon Web Services and Google Cloud.

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AI for Quitting Tobacco Initiative - World Health Organization

Robots have got pretty good at picking up objects. But give them something shiny or clear, and the poor droids will likely lose their grip. Not ideal if you want a kitchen robot that can slice you a piece of pie.

Their confusion often stems from their depth camera systems. These cameras shine infrared light on an object to detect its shape, which works pretty well on opaque items. But put them in front of a transparent object, and the light will go straight through and scatter off reflective surfaces, making it tricky to calculate the itemsshape.

Researchers from Carnegie Mellon University have discovered a pretty simple solution: adding consumer color cameras to the mix. Their system combines the cameras with machine learning algorithms to recognize shapes based on their colors.

[Read:How an AI learned to stitch up patients by studying surgical videos]

The team trained the system on a combination of depth camera images of opaque objects and color images of the same items. This allowed it to infer different 3D shapes from the images and the best spots to grip.

The robots can now pick up individual shiny and clear objects, even if the items are in a pile of clutter. Check it out in action in the video below:

The team admits that their system is still far from perfect. We do sometimes miss, but for the most part it did a pretty good job, much better than any previous system for grasping transparent or reflective objects, saidDavid Held, an assistant professor at CMUs Robotics Institute.

Im still not sure Id trust it with arazor-sharp kitchen knife. Unless I was really hungry and unwilling to leave the couch.

Published July 14, 2020 17:54 UTC

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This robot uses color cameras and AI to grab transparent objects - The Next Web

Artificial intelligence (AI) and machine learning (ML) have huge potential in many areas of business, particularly where there is a need to automate repetitive tasks.

This is of strategic importance for the IT security sector. Growing organisations dont always have the capability to scale up back-office compliance and security teams at a rate that is proportional to their expansion, leaving the existing function to do more with less; automating wherever possible reduces these pressures without compromising compliance.

Of course, AI and ML solutions are not new. We are already witnessing the success of adopting AI to automate everyday tasks such as identifying potential fraud, authenticating users and removing user access. It is ideal for repetitive tasks such as pattern analysis, source data filtering to determine factors such as whether something is an incident and, if so, whether it is critical, so tasks such as reviewing blocked emails, websites and images no longer have to be performed manually (ie by individuals).

AIs ability to simultaneously identify multiple data points that are indicators of fraud, rather than potential incidents having to be investigated line by line, also helps hugely with pinpointing malicious behaviour.

Predicting events before they occur is harder, but ML can help enterprises to stay ahead of potential threats using existing datasets, past outcomes and insight from security breaches with similar organisations all contribute to an holistic overview of when the next attack may occur. Fraud management solutions, security incident and event monitoring(SIEM), network traffic detection and endpoint detection all make use of learning algorithms to identify suspicious activity (based on previous usage data and shared pattern recognition) to establish normal patterns of use and flag outliers as potentially posing a risk to the organisation.

This capability is also critical in counteracting cyber attacks. Rather than manually trawling through a vast number of log files after an event has occurred, known intrusion methods can be identified in real time and mitigating action taken before much of the damage can occur.

To date, the main focus for the use of AI has been on the more technical security elements such as detection, incident management and other repeatable tasks. But these are early days, and there are many other areas that would benefit from its adoption. Governance, risk and compliance (GRC), for example, requires security professionals to crunch large amounts of data to spot risk trends and understand where non-compliance is causing incidents.

First discussions around AI saw it promise to revolutionise information security operations and reduce the amount of work that would need to be performed manually.

As outlined above, it has undoubtedly enabled new areas to be explored, while detecting attacks faster than any human manually looking through data. However, it is not a silver bullet and it comes with overheads, which are often forgotten.

It used to be that organisations installed logging systems that captured critical audit trails the challenge was in finding the time to look at the logs generated, a task that is now undertaken by AI scripts. However, while its easy enough to connect an application to an AI tool so that it can scan for suspicious activity, the AI system must first be set up so that it understands the format of the logs, and what qualifies as an event that needs flagging. In other words, to be effective, it needs training for the specific needs of each enterprise.

It is important not to underestimate these setup costs, along with the resource requirements to monitor the analytics AI provides. Incident management processes still need to be manually detailed so that once an event has been detected it can be investigated to make sure it wont impact the organisation.

Once AI is up and running it is a transformative tool for the organisation, but training it to interpret what action needs to be undertaken as well as rule out false positives is a time-consuming exercise that needs to be factored in to planning and budgets.

AI and ML introduce unprecedented speed and efficiency into the process of maintaining a secure IT estate, making them ideal tools for a predictive IT security stance.

But AI and ML cannot eliminate risk, regardless of how advanced they are, especially when there is an over-reliance on the capabilities of the technology, while its complexities are under-appreciated. Ultimately, risks such as false positives, as well as failure to identify all the threats faced by an organisation, are ever-present within the IT landscape.

Organisations deploying any automated responses therefore need to maintain a balance between specialist human input and technological solutions, while appreciating that AI and ML are evolving technologies. Ongoing training enables the team to stay ahead of the threat curve a critical consideration given that attackers also use AI and ML tools and techniques; defenders need to continually adapt in order to mitigate.

Successful AI and ML will mean different things to different organisations. Metrics may revolve around the time saved by analysts, how many incidents are identified, the number false positive removed, and so on. These should be weighed up against the resource required to configure, manage and review the performance of the tools. As with almost any IT security project, the overall value needs to be viewed through the eyes of the business and its role in achieving corporate objectives to reduce risk.

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Security Think Tank: Ignore AI overheads at your peril - ComputerWeekly.com

There are several industries that I believe will take off in the post-pandemic era. First and foremost are e-commerce and contactless shopping; this is an industry in which new protocols are expected to continue as social distancing has become mandatory.

Robotics and AI-based Automation in the Post Pandemic Era

Q&A with Anis Uzzaman, CEO and General Partner | Pegasus Tech Ventures

Pegasus Tech Ventures is a global venture capital firm based in Silicon Valley that invests in emerging technology companies around the world. We work with startups to expand into new markets globally. Our portfolio companies target disruptive opportunities in Artificial Intelligence, Robotics, IT, HealthTech, IoT, Big Data, Quantum Computing, FinTech, and other next-generation technologies. Pegasus manages about US$1.5 billion across 25 funds, on behalf of 35+ corporate partners. By helping entrepreneurs connect with corporations and enter new markets, Pegasus Tech Ventures bridges innovation ecosystems around the world. Pegasus also founded and sponsors the Startup World Cup, one of the biggest startup competitions in the world. Startup World Cup covers more than 60 regional locations across six continents; the Grand Finale in San Francisco offers a US$1 million-dollar investment prize to the winning team.

There are several industries that I believe will take off in the post-pandemic era. First and foremost are e-commerce and contactless shopping; this is an industry in which new protocols, such as contactless delivery methods, are expected to continue as social distancing has become mandatory. Startups including Nuro and Starship Technologies have taken it one step further and created fully autonomous vehicles that deliver to your door without human involvement. Such advances will get increasingly popular as COVID-19 requires the world to adapt to new sets of living standards. Well also witness a rise in popularity of E-Sports. Traditional sports-focused companies, including ESPN, have been doubling down on growing E-Sports businesses. We also see this trend in startups, with companies like Sleepr adapting its product roadmap to bring E-Sports to the forefront. Twitch, already an industry leader, has seen a surge in the first quarter of 2020, breaking its own records in hours watched and average concurrent viewership.

A few startups that are creating artificial intelligence-based robotics automation solutions include Osaro, Vicarious, and Kindred. These companies are applying different types of artificial intelligence to automate various tasks in the warehouse and distribution environment; this is key in ensuring that social distancing will continue to occur, while still properly maintaining the supply chain. Kindred.ai, in particular, has already deployed many solutions with popular brands including GAP and Nike. The retail sector has been hit particularly hard during this time. I anticipate there will also be an emphasis on accelerating automation of their operations to ensure they can better maintain operations in the future. Osaro is focused on both E-Commerce applications and food packaging, where there is a desperate need for more human-free solutions.

While there are a wide variety of tips I have in mind for startups at the moment, three of the best ones to begin with are:

Reconsider your financial spending and needs. What is the conversion on sales with your marketing campaign? What is your current sales productivity? How much cash do you have in the bank, and how long will it last based on the current burn rate? Companies need to determine the most essential expenditures required to reach minimum milestones during this environment.

Reset your stakeholders expectations. Now is also the time to talk with your investors, customers, and employees to adjust expectations. In the past, current investors and potential investors may have indicated a particular revenue milestone that you need to reach for the next round of equity financing. Have a discussion with them and brainstorm what is realistic given the new macroeconomic environment.

Come up with an adjusted fundraising plan. Startups will need to adjust their fundraising plans based on the new economic reality. If a startup doesnt include an adjusted plan and cannot explain how it will take the new business environment into account, then investors will have an increasingly difficult time getting approval from their investment committee to finance that company.

It is even more relevant today as startups need more revenue and corporations need to accelerate their innovation initiatives. As a way to address the market dynamic for startups and corporations, Venture Capital-as-a-Service (VCaaS), provides an optimal mix of capital and business value to startups through corporate fund networks. Pegasus Tech Ventures is providing startups with both flexible check sizes and business engagements with strategic corporate partners. Pegasus has partnered with 35+ corporations including ASUS, AISIN, and SEGA. For example, Pegasus and AISIN have recently partnered to help provide funding for autonomous vehicle technology company StradVision.

As VCs we want to bring together those who may have been deprived of investment opportunities due to their backgrounds or ethnicities. Diversification, within robotics specifically, is key to ensuring that future technologies hold no biases. VCs in particular have an opportunity in front of them to empower diverse startups, and to help the companies become future leaders. VCs should use their influence to put diverse groups at the front of peoples minds. Here at Pegasus, weve taken concrete steps to provide opportunities for people of all backgrounds and conduct our business with purpose - whether that means investing in women-led startups or black and Indigenous people of color-owned startups, or hosting our Startup World Cup with over 60 countries participating to help ensure that everyone is able to play on the same field.

About Anis UzzamanAnis Uzzaman, Ph.D. is the CEO & General Partner of Pegasus Tech Ventures, overlooking overall management, investments, and operations. Located in Silicon Valley, Pegasus Tech Ventures provides early stage to final round funding. Anis has invested in over 170 startups across North America, Europe, and Asia. Anis is also the Chairman of Startup World Cup, a global startup pitch competition with 50+ regional events across the 6 continents, leading up to $1,000,000 in investment prize.

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Humans and robots can now share tasks - and this new partnership is on the verge of revolutionizing the production line. Today's drivers like data-driven services, decreasing product lifetimes and the need for product differentiation are putting flexibility paramount, and no technology is better suited to meet these needs than the Omron TM Series Collaborative Robot. With force feedback, collision detection technology and an intuitive, hand-guided teaching mechanism, the TM Series cobot is designed to work in immediate proximity to a human worker and is easier than ever to train on new tasks.

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Robotics and AI-based Automation in the Post Pandemic Era - Robotics Tomorrow

WALTHAM, Mass.--(BUSINESS WIRE)--Wolters Kluwer, Health, a leading global provider of trusted clinical technology and evidence-based solutions, is recognized by Frost & Sullivan as a Frost Radar global leader in artificial intelligence (AI) for healthcare IT. The independent analysis evaluated a field of more than 200 healthcare IT companies and ranked Wolters Kluwer among the top 20 for continuous innovation and growth focusing on areas where AI solutions are most relevant for hospitals, physicians and payers.

In a market forecasted to reach more than $34 billion globally by 2025, Wolters Kluwer is one of the top growth performers in AI for healthcare IT and poised to move higher on the Radar, commented Koustav Chatterjee, report author and analyst for Frost & Sullivans Global Transformational Health team. In innovation metrics, Wolters Kluwer delivered remarkable results at scale for both payers and providers.

Wolters Kluwer is coupling the expansive knowledge of its trusted clinical experts with impactful AI solutions that target complex problems in healthcare. According to the Frost report, the top-right Radar positioning of Wolters Kluwer, adjacent to well-recognized tech giants, highlights its superior deep learning and NLP capabilities, and showcases how Wolters Kluwer is reimagining predictive clinical surveillance.

A Global Powerhouse for AI in Healthcare Frost & Sullivan forecasts Wolters Kluwer will expand its AI-enabled healthcare IT footprint, working closely with large health systems, government agencies, and leading start-ups from Europe, the Middle East, and Southeast Asia in the next 2 to 3 years. Frost sees growth from healthcare stakeholders with the need and incentive to embrace full-fledged AI to improve clinical efficacy, augment financial performance, and streamline operational agility.

AI is deeply woven into the Wolters Kluwer DNA and it fully spans our Health solutions, commented Jean-Claude Saghbini, Chief Technology Officer, Wolters Kluwer, Health. The Frost Radar report validates years of effort and investment building a world-class AI ecosystem with a unique combination of data scientists, clinicians, and product teams that can make a meaningful impact on healthcare.

Indicators of this AI innovation in Health solutions include:

Other rapid response innovations in clinical content and data science:

To learn more, download the Frost Radar report: Artificial Intelligence for Healthcare IT, Global, 2020.

Read this story on our website.

About Wolters Kluwer

Wolters Kluwer (WKL) is a global leader in professional information, software solutions, and services for the clinicians, nurses, accountants, lawyers, and tax, finance, audit, risk, compliance, and regulatory sectors. We help our customers make critical decisions every day by providing expert solutions that combine deep domain knowledge with advanced technology and services.

Wolters Kluwer reported 2019 annual revenues of 4.6 billion. The group serves customers in over 180 countries, maintains operations in over 40 countries, and employs approximately 19,000 people worldwide. The company is headquartered in Alphen aan den Rijn, the Netherlands.

Wolters Kluwer provides trusted clinical technology and evidence-based solutions that engage clinicians, patients, researchers and students in effective decision-making and outcomes across healthcare. We support clinical effectiveness, learning and research, clinical surveillance and compliance, as well as data solutions.

For more information about our solutions, visit https://www.wolterskluwer.com/en/healthand follow us on LinkedIn and Twitter @WKHealth.

For more information, visit http://www.wolterskluwer.com, follow us on Twitter, Facebook, LinkedIn, and YouTube.

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Frost & Sullivan Radar Ranks Wolters Kluwer as a Top 20 AI Innovation Leader in Healthcare IT - Business Wire

Last Chance: Register for Transform, VB's AI event of the year, hosted online July 15-17.

At our AI-focusedTransform 2020event, taking place July 15-17entirely online, VentureBeat will recognize and award emergent, compelling, and influential work through our second annual VB AI Innovation Awards. Drawn from our daily editorial coverage and the expertise of our nominating committee members, these awards give us a chance to shine a light on the people and companies making an impact in AI.

Here are the nominees in each of the five categories NLP/NLU Innovation, Business Application Innovation, Computer Vision Innovation, AI for Good, and Startup Spotlight.

Dr. Dilek Hakkani-Tur

A senior principal scientist at Amazon Research and faculty member at the University of California, Santa Cruz, Dr. Hakkani-Tur currently works on solving natural dialogue for Amazons Alexa AI. She has researched and worked on natural language processing, conversational AI, and more for over two decades, including stints at Google and Microsoft. She holds dozens of patents and has written or co-authored more than 200 papers in the area of natural language and speech processing. Recent work includes improving task-oriented dialogue systems, increasing the usefulness of open-domain dialogue responses, and repurposing existing data sets for dialogue state tracking for natural language generation (NLG).

BenevolentAI

BenevolentAIs mission is to use AI and machine learning to improve drug discovery and development. The amount of available data is overwhelming, and despite a steady stream of new research, too many pharmaceutical experiments fail today. BenevolentAI helps by accelerating the indexing and retrieval of medical papers and clinical trial reports about new treatments for diseases that dont have cures. Fact-based decision-making is essential everywhere, but for the pharmaceutical industry, the facts just need to be harvested in a synthetic, relevant, and efficient way.

StereoSet

Research continues to uncover bias in AI models. StereoSet is a data set designed to measure discriminatory behaviors like racism and sexism in language models. Researchers Moin Nadeem, Anna Bethke, and Siva Reddy built StereoSet and have made it available to anyone who makes language models. The teams maintains a leaderboard to show how models like BERT and GPT-2 measure up.

Hugging Face

Hugging Face seeks to advance and democratize natural language processing (NLP). The company wants to contribute to the development of technology in this domain by growing the open source community, conducting research, and creating NLP libraries like Transformers and Tokenizers. Hugging Face offers free online tools anyone can use to leverage models such as BERT, XLNet, and GPT-2. The company says more than 1,000 companies use its tools in production, including Apple and Microsofts Bing group.

Jumbotail

Jumbotails technology updates traditional mom-and-pop stores in India, often known as kirana stores, by connecting them with recognized brands and other high-quality product producers to help transform them into modern convenience stores. Jumbotail does so without raising the cost to customers by collecting and mining millions of data points in real time every day. Thanks to its AI backend, Jumbotail became Indias leading online wholesale food and grocery marketplace, with a full stack that includes integrated supply chain and logistics, as well as an in-house financial tech platform for payments and credit. The insights and tech developed around this new business model empower producers and customers, and Jumbotail is poised to expand to other continents.

Codota

Codota is developing a platform powered by machine learning that suggests and autocompletes Python, C, HTML, Java, Scala, Kotlin, and JavaScript code. By automating routine programming tasks that would normally require a team of skilled developers, the company is helping reduce the estimated $312 billion organizations spend on debugging each year. Codotas cloud-based and on-premises solutions, which are used by developers at Google, Alibaba, Amazon, Airbnb, Atlassian, and Netflix, complete lines of code based on millions of programs and individual context locally, without sending any sensitive data to remote servers.

Rasa

Rasa is an open source conversational AI company whose tools enable startups to build their own (close to) state-of-the-art natural language processing systems. These tools some of which have been downloaded over 3 million times bring AI assistants to life by providing the technical scaffolding necessary for robust conversations. Rasa invests in research to create conversational AI, furnishing developers at companies like Adobe, Deutsche Telekom, Lemonade, Airbus, Toyota, T-Mobile, BMW, and Orange with solutions to understand messages, determine intent, and capture key contextual information.

Dr. Richard Socher

Dr. Richard Socher is probably best known for founding MetaMind, which Salesforce acquired in 2016, and for his contribution to the landmark ImageNet database. But in his most recent role as chief scientist and EVP at Salesforce (he just left to start a new company), Socher is responsible for bringing forth AI applications, from initial research to deployment.

Platform.ai

To help domain experts without AI expertise deploy AI products and services, Platform.ai offers computer vision without coding. Its an end-to-end rapid development solution that uses proprietary and patent-pending AI and HCI algorithms to visualize data sets and speed up labeling and training by 50-100 times. The goal is to empower companies to build good AI. Platform.ai can count big-name brands like GE, Claro, and Mattel as customers. The companys founders include chief scientist Jeremy Howard, who is also the founding researcher of deep learning education organization Fast.ai and a professor at the University of San Francisco.

Abeba Birhane and Dr. Vinay Prabhu

In their powerful work, Large image datasets: A pyrrhic win for computer vision?, researchers Abeba Birhane, Ph.D. candidate at University College Dublin, and Dr. Vinay Prabhu, principal machine learning scientist at UnifyID, examined the problematic opacity, data collection ethics, labeling and classification, and consequences of large image data sets. These data sets, including ImageNet and MITs 80 Million Tiny Images, have been cited hundreds of times in research. Birhane and Prabhus work is under peer review, but it has already resulted in MIT voluntarily and formally withdrawing the Tiny Images data set on the grounds that it contains derogatory terms as categories, as well as offensive images, and that the nature of images in the data set makes remedying it unfeasible.

Dr. Dhruv Batra

An assistant professor in the School of Interactive Computing at Georgia Tech and a research scientist at Facebook AI Research, Dr. Dhruv Batra focuses primarily on machine learning and computer vision. His long-term research goal is to create AI agents that can perceive their environments, carry natural-sounding dialogue, navigate and interact with their environment, and consider the long-term consequences of their actions. Hes also cofounder of Caliper, a platform designed to help companies better evaluate the data science skills of potential machine learning, AI, and data science hires. And he helped create Eval.ai, an open source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale.

Ripcord

Ripcord offers a portfolio of physical robots that can digitize paper records, even removing staples. Employing computer vision, lifting and positioning arms, and high-quality RGB cameras that capture details at 600 dots per inch, the companys robots are able to scan at 10 times the speed of traditional processes and handle virtually any format. Courtesy of partnerships with logistics firms, Ripcord transports files from customers such as Coca-Cola, BP, and Chevron to its facilities, where it scans them and either stores them to meet compliance requirements or shreds and recycles them. The companys Canopy platform uploads documents to the cloud nearly instantly and makes them available as searchable PDFs.

Machine Learning Emissions Calculator

Authors Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres built an online calculator so anyone can understand the carbon emissions their research generates. Machine learning research demands high compute resources, and even as the field achieves key technological breakthroughs, the authors of the calculator believe transparency about the environmental impact of those achievements should be generalized and included in any paper, blog post, or publication about a given work. They also provide a simple template for standardized, easy reporting.

Niramai

Niramai developed noninvasive, radiation-free early-stage breast cancer detection for women of all age groups using thermal imaging technologies and AI-based analytics software. The company works with various government and nonprofit entities to enable low-cost health check-ups in rural areas in India. Prevention and early detection are key to improving the outcomes of cancers, but health centers are not always equipped with expensive screening machines. Because thermal imaging is safe, cost-effective, and easy to deploy, it can improve early screening in low-tech facilities around the world.

Dr. Pascale Fung

Dr. Pascale Fung is director of the Centre for AI Research (CAiRE) at the Hong Kong University of Science and Technology (HKUST). Among other accolades and honors, Fung represents the university at Partnership on AI and is an IEEE fellow because of her contributions to human-machine interactions. Through her work with CAiRE, she has helped create an end-to-end empathetic chatbot and a natural language processing Q&A system that enables researchers and medical professionals to quickly access information from the COVID-19 Open Research Dataset (CORD-19).

Dr. Timnit Gebru

Dr. Timnit Gebru continues to be one of the strongest voices battling racism, misogyny, and other biases in AI not just in the actual technology, but within the wider community of AI researchers and practitioners. Shes the co-lead of Ethical AI at Google and cofounded Black in AI, a group dedicated to sharing ideas, fostering collaborations, and discussing initiatives to increase the presence of Black individuals in the field of AI. Her work includes Gender Shades, the landmark research exposing the racial bias in facial recognition systems, and Datasheets for Datasets, which aims to create a standardized process for adding documentation to data sets to increase transparency and accountability.

Relimetrics

Relimetrics develops full-stack computer vision and machine learning software for QA and process control in Industry 4.0 applications. Unlike many other competitors in the field of visual inspection, Relimetrics proposes an end-to-end flow that can be adopted by large groups, as well as smaller manufacturers. Industry 4.0 is associated with a plethora of technological stacks, but few are able to scale to large and small manufacturers across multiple industries yet remain simple enough for domain experts to deploy them, which is where Relimetrics comes in.

Dr. Daniela Braga, DefinedCrowd

DefinedCrowd creates high-quality training data for enterprises AI and machine learning projects, including voice recognition, natural language processing, and computer vision workflows. The company crowdsources data labeling and more from hundreds of thousands of paid contributors and passes the massive curation on to its enterprise customers, which include several Fortune 500 companies. The startups cofounder and CEO, Dr. Daniela Braga, has credentials in speech technology and crowdsourcing dating back nearly two decades, including nearly seven years at Microsoft that included work on Cortana. She has led DefinedCrowd through several rounds of funding most recently, a large $50.5 million round in May 2020.

Flatfile

Flatfile wants to replace manual data janitoring for enterprises with its AI-powered data onboarding technology. Flatfile is content agnostic, so a company in essentially any industry can take advantage of its Portal and Concierge platforms, which are able to run on-premises or in the cloud. Flatfile has completed two funding rounds, one of which wrapped up in June 2020. As of September 2019, the company had attracted 30 customers with essentially no paid advertising. Less than a year later, it had 400 companies on its waitlist, ranging from startups up to publicly traded companies.

DoNotPay

DoNotPay, founded by British-born entrepreneur Josh Browder, offers over 100 bots to help consumers cancel memberships and subscriptions, fight corporations, file for benefits, sue robocallers, and more. While much of the companys automation engine is rules-based, it leverages third-party machine learning services to parse terms of service (ToS) agreements for problematic clauses, such as forced arbitration. To address challenges stemming from the pandemic, DoNotPay recently launched a bot that helps U.S.-based users file for unemployment. In the future, the startup plans to bring to market a Chrome extension that will work proactively for users in the background.

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The nominees for the VentureBeat AI Innovation Awards at Transform 2020 - VentureBeat

Last Chance: Register for Transform, VB's AI event of the year, hosted online July 15-17.

I cant wait until Transform 2020 starts tomorrow. Its our flagship event for enterprise decision-makers to learn how to apply AI.

One of our goals at VentureBeat is to create a new kind of town square for enterprise decision makers to learn about transformative technology and transact. And the practice of AI is where were going deep. AI is the most powerful technology in enterprise today, and VentureBeat is the leading publication covering AI news.

So its important that VentureBeat create a virtual platform where that community can come together, and have conversations. Since weve already been digital with our news offering, we are able to pivot fully virtual and bring the same, if not more value to our AI events.

Im pretty proud of what weve done, including the one-to-one meeting feature for executives who would like to connect with each other to get business done.

Im excited about the agenda we have for the three days. Were focused on the top application areas: for example, conversational AI and computer vision and edge IoT.

In my opening remarks tomorrow, Ill provide preliminary results of our AI survey, and an overview of the big trends were seeing shaped by the hundreds of conversations we had with executives while preparing for the show.

Above: CTO Twitter Parag Agrawal

Image Credit: Twitter.com

Ill personally be interviewing Twitter CTO Parag Agrawal, about how Twitter has been using AI/ML at scale to foster a more constructive public discourse and to flag harmful speech. Twitter has been in the hot-seat lately forced to label tweets from President Donald Trump that it perceived as misleading or harmful. And how does Twitter flag offensive tweets accurately, when researchers have shown that leading AI models processing hate speech are often inaccurate or biased? Agarwal will discuss how Twitter blends AI/ML, policy, and product in a dynamic environment where it has to counter adversaries who often use state of-the-art conversational AI technology themselves.

Well address the gap in perceived importance of ethics and accuracy in AI. In our AI survey, most of our practitioner respondents said they believe enough is being done at their companies to counter bias (ethnic, gender, etc.) in implementing AI models. This contrasts with what were hearing from professionals from underrepresented backgrounds. On Thursday morning, well have an hour-long roundtable on the topic of Diversity and Inclusion in AI led by four Black professionals, which I highly recommend attending if you can get in (it will be capped). This will be a strong session, and eye-opening for anyone thinking enough is being done to counter bias in AI. It will follow our Women in AI (virtual) Breakfast, where well have Timnit Gebru and other leaders represented.

One area of particular controversy is facial recognition technology, where study after study has shown that it is less accurate on underrepresented populations. At our AI showcase at Transform, Trueface, a facial recognition company, will be releasing a new product, and Hari Sivaraman, Head of AI Content Strategy, VentureBeat, will have a crossfire Q&A with Trueface CEO Shaun Moore about how it is using facial recognition and its purported accuracy.

Theres too much happening to summarize entirely here. The AI Innovation Awards tomorrow evening, the Expo, the intimate roundtables But it does look like Transform will be the biggest AI event for business executives this year, given that most other events were canceled or postponed. We have almost 3,000 people registered, double the number from last year.

And of course, none of this is possible without our great sponsors folks like Dataiku, Intel, CapitalOne, Nvidia, Modzy, Cloudera, DotData, Twohat, Dell, Inference Solutions, Anaconda, Conversica, SambaNova, Xilinx, Globant, and more. Many of their executives will be participating as speakers, alongside speakers from some great brands like Walmart, Uber, Google, Adobe, Chase, Goldman Sachs, Visa, PayPal, Intuit, CommonSpirit Health, GE Healthcare, Pfizer, Pinterest, Slack, Yelp, LinkedIn, eBay, and Salesforce.

Looking forward to seeing you there virtually! (Register here: vbtransform.com)

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Unpacking AIs power and its controversies at Transform 2020 - VentureBeat