Monthly Archives: July 2021

Smart Career Moves In AI

Businesses are building AI capabilities into their products, everything from automobiles to consumer electronics. Health-care organizations are using AI to deliver better services to patients while manufacturers are adding AI to their operational technology to improve efficiency. And its hard to find an IT vendor that isnt using AI in some way to make its technology smarter.

Artificial intelligence has been a hot technology in recent years and thats spurred demand for engineers and software developers who can design and develop AI and machine learning algorithms and code and build them into everything from sophisticated IT systems to everyday consumer products.

[RELATED: Artificial Intelligence Week 2021]

Job search and employment website Indeed recently took a look at AI job postings to see which AI-related jobs are the most in demand and are paying the highest median salaries in the U.S. Heres what it found.

Link:

The 10 Most In-Demand AI Jobs And Their Salaries: Indeed - CRN

Biological image processing and analysis can often be laborious and complex tasks for researchers. Aivia aims to help researchers tackle the most challenging imaging applications using artificial intelligence (AI)-guided image analysis and visualization solutions.Technology Networks spoke with Dr. Luciano Lucas, director at Leica Aivia, to learn more about the challenges of biological image analysis and how AI can help to overcome them. In this interview, Dr. Lucas also explains some of the potential barriers to wider adoption of AI in laboratories and shares his views on where AI microscopy may be headed in the future.

Anna MacDonald (AM): What challenges do researchers face when undertaking biological image analysis? How can AI help to address these issues?Dr. Luciano Lucas (LL): Researchers in the biopharma/life sciences spaces are faced with a wide range of problems when it comes to image processing and image analysis. The key issues we have identified and are working on) are:1) Development, implementation and accessibility to state-of-the-art AI technology (AI microscopy). This type of technology enables the completion of previously impossible to run experiments. However, AI microscopy is a new discipline which requires further research, validation and characterization. We and others in the community are very active on this important task. After four and a half years of R&D we feel confident enough to release to the public some of the work we have done as either pre-trained deep learning models (see our 3D RCAN paper and Aivia DL Model Library) or enabling software tools that allows everyone to leverage some key AI microscopy technology (e.g. AiviaCloud).

2) Inherent image quality. Image acquisition and image analysis are decoupled from each other in time. This often results in the creation of large amounts of image data that are not good enough for analysis.

3) Data size. This poses all sorts of issues on both the visualization and analysis fronts.

4) Result accuracy and reproducibility. Both are an essential part of the scientific discovery process.

5) Tool complexity. Making tools that are easy to learn and use is essential to adoption this is often under appreciated.

Our research work is primarily focused on item one above, but we also have active internal R&D projects to address the rest. As we address the key topics mentioned above we strive to improve the rate of scientific discovery based on image data. We believe this can be achieved by improving how we (humans) interact with software and hardware. Present day tools ignore the fact that researchers are experts in biology (or similar disciplines) and may have very limited expertise in microscopy, image analysis and/or data science/machine learning (ML)/deep learning (DL)/AI. By creating tools that acknowledge and leverage the biologists expertise we can create intelligent tools that learn (about biology) from the user. Such tools would gradually learn what a cell is and what it can look like in multiple scenarios. Ultimately, the software/hardware should be able to autonomously do the imaging and image analysis, thus allowing the researcher to focus on the creative and critical thinking portion of the scientific discovery process.

AM: How easy is it for laboratories to adopt AI? Are there any barriers that need to be overcome?LL: From the point of view of availability, it is easy. Aivia is a key example of a professionally developed and supported software platform that can be used by anyone. There are several open source projects that offer powerful technical solutions in this space too. The issue/problem for wider adoption is tool and technology complexity. AI is a new topic within the microscopy/biomedical sciences community. Thus, there are very few experts and fewer good tools.In the last three years we have seen a major increase in pre-prints and peer-reviewed publications using AI for microscopy as well as the creation of several high-profile courses and symposia on the topic (see the AI Microscopy Symposium). I expect the number of publications to continue to increase in the coming years as this type of approach splits out of the labs/groups/companies that have been pioneering it and become mainstream. The leaders in this community will need to continue their outreach and educational activities in turn this will help solve the key issues mentioned above.

It is key to create tools (software and hardware) that clearly show the value of AI for microscopy. Todays best AI-powered tools can achieve a lot in the hands of ML/DL experts but, for the most part, are not easy to use for non-experts in this space. Our team is very aware of this and is focused on creating tools (Aivia/AiviaWeb/AiviaCloud) that remove the complexity while delivering the full power of AI for microscopy applications.

AM: Can you tell us more about Aivia and what sets it apart?LL: Aivia makes AI microscopy accessible to all. From image restoration (and super resolution) to image segmentation and virtual staining, we can do it all in one easy-to-use platform. Aivia is also great for large (multi TB) data sets and has several good solutions for automation and reproducibility.

AM: What do you see in store for the future of AI in microscopy?LL: It is a true pleasure to work in this field nearly every day one comes across new ideas with significant potential to be transformative. Below are some of my favorites (not all of them used in the microscopy world at least not yet).

GPT3

Transformers

Flood filling networks

Smart microscopy

U-net

CARE

Neuromorphic processing units

Optical deep learning

Virtual staining

In the next few decades, we will gradually move from AI solutions/tools that are good (i.e. human level performance) at System 1 learning and thinking, to AI agents that can do System 2 learning and thinking. This is the true challenge both in microscopy and more generally. Humans will likely remain far superior to AI agents for tasks that require the integration and consideration of multiple, incomplete, multi-domain data sources. As we create better AI agents that can act more often in a System 2 way, humans will be able to dedicate more of their time to creative and innovative tasks, e.g. creating scientific hypotheses, designing experiments to test those and interpreting the insights provided by the AI agents.

Dr. Luciano Lucas was speaking to Anna MacDonald, science writer for Technology Networks.

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AI in Microscopy: Opportunities, Challenges and the Future - Technology Networks

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LinkSquares, a contract management and analytics tool for legal and finance teams, today announced that it raised $40 million in a series B funding round led by Sorenson Capital. The company, whose total funding stands at $61.4 million, says itll use the financing to expand its workforce, advance its technology, and develop strategic business partnerships.

During the pandemic, legal departments and contract negotiators faced a critical period of transformation. Legal was expected to contribute data-driven analyses while contending with the gap between executed contract analytical platforms and legal request business process flows. More than half of the worlds major companies face lost revenue and missed business opportunities as a result of inefficiencies in their handling of contracting processes, according to an EY Law survey.

Just this past year, legal and compliance teams raced to analyze their business exposure to major events across PDFs, paper contracts, and other documents. The pandemic caused a surge in interest from prospective customers who needed digital contract management. These companies couldnt afford to use on-premises solutions locked away in an office anymore, and they needed our help to ensure fully remote implementations that were quick and painless, and we came through, LinkSquares CEO Vishal Sunak told VentureBeat via email.

According to Sunak, the inspiration for LinkSquares came during his experiences with the manual work associated with contracts over the course of the acquisition business continuity firm Datto planned during Sunaks time at Backupify, a cloud data backup company. Datto hoped to migrate Backupifys customer data to its cloud infrastructure, but the team first had to understand each signed customer contract and determine if Datto had the right to move the data without permission.

Above: The LinkSquares platform.

Image Credit: LinkSquares

The idea to review each contract, read the provision related to data transfer, and store the answer seemed straightforward at first. In reality, because Backupify had negotiated more than 2,000 contracts, the act of finding all the contracts and looking for the provision language was an impossible undertaking, Sunak explained. When our team explored the types of contract management products available for post-signed contracts, there was a spark of innovation: most of the tools that could surface answers and insights from executed agreements focused on contracts that hadnt been finalized yet, mainly in the pre-signature stage. And so, LinkSquares and its AI for signed contracts was born.

LinkSquares platform performs searches for keywords, contract terms, and phrases across documents using AI. It extracts data from contracts (e.g., parties, effective and termination dates, payment terms, governing states, and limitations and liabilities), and its email-based notification function reminds teams of important dates and obligations. Optical character recognition transforms scanned PDFs into a searchable format, while custom user roles let admins control data access. And a clause library enables real-time searches for contract clauses.

Were starting to understand the real benchmarks of what is being agreed to and how people are agreeing to it in their contracts. Because were an AI company were starting to see macro trends in legal language emerge, Sunak said. Modern legal teams need to be data-driven and move beyond their perception as a cost center and gatekeeper. Corporate counsels and mergers and acquisitions teams rely on LinkSquares to elevate their internal value, reputation, confidence, and productivity by eliminating time spent on manual and ineffective processes.

LinkSquares customers include over 400 brands like Fitbit, Twilio, TGI Fridays, Wayfair, and Cogito. Growth over the last two years exceeded 1,000%, and the company recently announced a technology partnership with Xerox PARC (Palo Alto Research Center), the R&D lab behind laser printing and electronic ink.

Cogito estimates saving $30,000 per year [with LinkSquares, while] Asurion reduced time spent on searching for information by 50%, Sunak said. We overcame a lot of adversity this past year with employees and customers and we needed new levels of empathy and flexibility for a workforce that was 100% remote overnight. And we had customers who had a lot of uncertainty about their financial situations, so I personally worked something flexible out with several. That way, no one had to choose between paying their own employees and maintaining access to their critical legal insights.

Sunak expects that Boston, Massachusetts-based LinkSquares annual recurring revenue will grow 100% year-over-year by the end of 2021, up from well over $10 million. Other backers in the companys latest round included Catalyst Investors, Xerox, Bottomline Technologies, DraftKings founders and key legal and compliance executives, Hyperplane Venture Capital, MassMutual Ventures, and First Ascent Ventures.

Theres no shortage of startups developing AI-driven contract creation and management tools. Others in the $2.9 billionmarket includeConcord, which raised $25 million for its digital contract visualization and collaboration tools in 2019. Thats not to mention Icertis, which recently snagged $115 million; DocuSign, which invested $15 million in AI contract discovery startupSeal Software;andEvisort, which nabbed over $15 million to develop its solutions.

See the article here:

LinkSquares nabs $40M to expand its AI-powered contract platform - VentureBeat

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GovExec Daily: Using AI to Solve Problems in Health Care and Beyonfd - GovExec.com

People of all ages and mixed ethnicity groups standing together

In a recent article, WIRED senior writer Tom Simonite talked to Kate Crawford, author of Atlas of AI, to explore the ethical issues facing artificial intelligence and machine learning technologies.

Were relying on systems that dont have the sort of safety rails you would expect for something so influential in everyday life, notes Crawford. There are tools actually causing harm that are completely unregulated.

When people that arent in the industry hear me say that artificial intelligence and machine learning can become forces for positive change in society, they ask me to explain why these technologies have been mired in controversy for more than a decade. And why ethical issues seem to be getting worse versus getting better.

Indeed, in recent years several high-profile cases of ML technologies causing harm to marginalized parts of society have captured headlines. Household name brands like Amazon, Apple, Facebook, Google have been accused of algorithmic bias, thus affecting society. As a result, there is a growing sentiment that systems designed to improve everything from peoples financial lives to their physical well-being have become a threat to many populations already suffering.

To answer these questions, I draw from my own experience and observations in the field. Theres never one answer to such a complex set of issues. Still, one contributor is that, historically, the teams responsible for the systems that make millions of life-changing decisions every second have been largely homogeneous, built without extreme care for whether they are reflective of all of society.

In other words, there is an entire generation of data scientists and engineers in our industry that are building systems that impact groups of society they dont understand.

To be fair, theres nothing wrong with a company reflecting on solving some of the worlds most vexing problems by hiring the best and brightest talent to do so. However, unless theres a conscious effort to build diversity into the fabric of an organization, the result is a pool of talent in the ML community that isnt being discovered and nurtured, and their skills and experience are being wasted.

More importantly, teams that lack a diversity of backgrounds, experiences, and perspectives not only perpetuate workplace inequality but also serve as a barrier to solving many of the problems that ML and AI technologies have the potential to address.

Although no single company or team can solve the ethical AI dilemma, the hope for all companies in our industry is that they embrace fairness, transparency, and accountability in their hiring and R&D processes so developments in AI advance positive outcomes for all people and societies.

Some recent developments suggest that there is good reason to believe many companies will adopt these principles. More and more organizations are investing in teams to ensure algorithmic accountability and ethics with an ultimate eye towards improving how their products impact the world.

Yet, these steps are just the beginning. We know that implementing systems that are free from bias and ethical concerns is essential; however, achieving this goal requires direct action in the following areas:

Conclusion

In the world of AI and machine learning, we are quickly learning that data and models can often obscure the hard truths of a persons lived experience. This is particularly true if the models are built by teams that are not representative of race, gender, sexual orientation, or socioeconomic status. Suppose we imagine different outcomes, have the readiness to pursue them, and start with the people behind the products first. In that case, we can create a new reality where ML and AI technologies truly serve all people fairly and without harm.

Follow this link:

Unleashing The Power Of A Diverse Team To Build More Ethical AI Technologies - Forbes

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The thing about fraud is that its constantly changing looking at a past attack doesnt guarantee the next attack will look the same or target the same kind of victim and defenders have to continuously adapt. Visa utilizes artificial intelligence to analyze all of the transactions that go across the network and track large-scale transactional changes as part of its fraud detection efforts, Melissa McSherry, Visas senior VP and global head of data, security, and identity products, said at VentureBeats Transform 2021 virtual conference on Monday.

Visa scores all of the transactions that go across the Visa network, which allows the company to define a set of behaviors that would be considered normal. The team is constantly updating the models view of history and updates the model itself to reflect the data on a fairly regular basis, McSherry said.

The fraudsters do not stand still. And theyre always looking to innovate, McSherry said in a conversation with Jana Eggers, the CEO of synaptic intelligence company Nara Logics.

Being able to detect changes in the data is useful for authentication, McSherry said. A single phone and email address pair is likely associated with a legitimate transaction, especially if that same pair has been used for a lot of transactions. The next transaction that comes through with the pair will also likely be tracked as legitimate. But if that one phone number is associated with 500 email addresses, it is more likely that all the email addresses are associated with compromised accounts and that the phone number is not associated with a real identity, either.

It is absolutely the case that the data is constantly evolving, but we take advantage of the velocity of the enormous amount of data that we get and try to evolve our perspective with it, McSherry said.

Everyone knows Visa has an enormous amount of data, and McSherry was able to shed some light on how Visa uses it. Visa has been using neural networks for fraud detection since the 1990s. Eventually, the self-learning technology updated the frame of what was normal to identify big deviations in model distributions. More recently, the company is using convolution neural networks (CNN) and recurrent neural networks (RNN) to improve pattern recognition across the network. They arent just used as models but also around the models to identify areas that require more scrutiny or highlight changes that need to be made to the model, McSherry said.

Visa uses generative adversarial networks (GAN) to create virtual fraudsters and pit them against the anti-fraud tools to identify gaps in the fraud-detection models, McSherry said. The gaps can also be in tools provided by partners or in the business logic.

My experience has been that sometimes things dont work exactly the way that you think that theyre going to work the very first time that you use them, McSherry said. Consequently, new methods will be used in parallel or just for monitoring until they are better understood.

Incorporating AI requires commitment and follow-through, McSherry said. Visa consistently sees a 20% to 30% lift for advanced AI methods over more garden-variety technologies, but this requires heavy investment. Stakeholders need to remain engaged and focused because the first few attempts may not work exactly as planned. Experimentation and patience are key.

The first and most important thing is just making sure that the problem itself will benefit from those kinds of lifts, McSherry said. Its just really helpful if everybody understands that the value on the other side [of the implementation] is really worth quite a lot.

Having personnel learn about newer techniques will allow businesses to get the most out of machine learning. While it makes sense to hire new people with strong AI backgrounds, Visa also gave existing employees who understood the business the opportunity to experiment and learn new techniques.

I think that when people who really have the business context and long-term pride in the quality of the product [are combined] with a really good understanding of AI techniques, thats when you get something really special, McSherry said.

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Visa on using advanced AI such as unsupervised learning to fight fraud - VentureBeat

A Double-Edged Sword

Artificial intelligence is a double-edged sword when it comes to cybersecurity, with defenders using it to respond to and predict threats and attackers using it to launch even more refined attacks. For example, AI algorithms can send spear phishing tweets (personalized tweets sent to targeted users to trick them into sharing sensitive information) six times faster than a human and with twice the success.

The enlargement of attack surface and the increased sophistication of attacks has made AI a key weapon in thwarting cyberattacks, Capgemini found. Cyber analysts are finding it increasingly difficult to effectively monitor current levels of data volume, velocity, and variety across firewalls, prompting organizations to turn to artificial intelligence.

[RELATED: Artificial Intelligence Week 2021]

In fact, Capgemini found that 61 percent of organizations acknowledge they wouldnt be able to identify critical threats without AI. The increases in cyberattacks that can quickly compromise critical operations within an enterprise also require enhanced capabilities that can best be provided through AI, according to Capgemini.

From identifying fundamental traits that numerous threats share to classifying and responding to well-camouflaged malware to advising how organizations should allocate security resources, heres a look at how 10 AI security companies are making the world a safer place.

Read the rest here:

The 10 Hottest AI Security Companies You Need To Know - CRN