The COVID-19 pandemic brings with it increased focus on Artificial Intelligence ("AI") as developers rush to create software, such as contact tracing software, that can help businesses reduce the risks for employees returning to work. Before businesses acquire AI technology, owners and human resources professionals must consider how to balance being proactive with protecting employee privacy. There are numerous provisions they should incorporate into their contracts with their software developers. Some of these provisions include:

Comprehensive Testing

When contracting for AI, the customer should be particularly focused on documenting the level of testing to be provided. Generally, the more robust the description of the testing, the better. At a minimum, this description should include: the number of rounds of testing, the process for testing, what the minimum sample size will be for each round of testing, and who is involved in creating the test environment. In addition, the customer should ask the vendor to contractually commit to describing the remedies if the testing does not result in adequate work product. The parties need to define exactly what constitutes acceptance, and whether ongoing testing is necessary or appropriate, particularly as the AI adapts and learns from itself.

Security

Security is currently one of the fastest evolving areas of information technology law. When contracting for AI, it is important to have standards that can adapt to this ever-changing environment. In order to do this, it is helpful to incorporate a requirement that the vendor comply with industry security standards such as ISO-27001 and OWASP-Top 10 (for web applications). Businesses should also state any specific technical requirements related to security necessary to protect the customers IT environment, as well the whereabouts and other data associated with its employees and the locations of its customers. Finally, requiring adequate cyber-insurance that meets the risk level of the environment is also prudent.

Data Privacy

Customers should be wary that AI may transform data that was once anonymous into data that is decipherable. Also, there is a complex set of data privacy laws in effect in the United States and even more so globally. All vendors should contractually agree to comply with any such applicable laws. Customers should also consider putting limitations on how vendors can use data, particularly outside of providing the services to the contracting customer.

Minimizing Risk

Most vendors require a cap on consequential damages, but in AI contracts this provides additional challenges as much of the risk to the customer lies with items commonly considered to be consequential damages. There are several ways to address this problem. One way is to redefine what constitutes direct damages. A second way is to negotiate exceptions to caps for specified items such as: breaches of privacy/data security, failure to comply with threshold requirements, and allegations of bias due to algorithm data use.

This article is meant to share a few ideas for contracting for AI. As with any contract, you should contact a lawyer understanding the nuances of the subject matter particular to your situation prior to signing it.

For more information, please contact Diana J.P. McKenzie, partner & chair, Information Technology & Outsourcing Practice Group at HunterMaclean, dmckenzie@huntermaclean.com or Nicole Pope, attorney at HunterMaclean, npope@huntermaclean.com.

For other expert advice on taxes, retirement accounts, benefits, and liability insurance, go to savannahnow.com/beacon.

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COVID-19 and Artificial Intelligence: How the pandemic has re-ignited a focus on the software - Savannah Morning News

The human brain has advanced over time in countering survival instincts, harnessing intellectual curiosity, and managing authoritative ordinances of nature. When humans got an idea about the dynamics of the environment, we started with our quest to replicate nature.

While the human brain discovers ways to go beyond our physical capabilities, the combination of mathematics, algorithms, computational methods, and statistical models accumulated momentum after Alan Mathison Turing built a mathematical model for biological morphogenesis, and published a seminal paper on computing intelligence.

Today, AI has developed from data models for problem-solving to artificial neural networks, a computational model predicated on the structure and functions of human biological neural networks.

The brain, customarily perceived as an organ of the human body, should be understood as a biologically predicated form of artificial intelligence (AI). This proposition was surmised by the progenitors of AI in the 1950s, though it has been generally side-lined over the course of AIs history. However, developments in both neuroscience and more conventional AI make it fascinating to consider the issue anew.

The history of neuroscience has shown both tendencies from its inception, not least in terms of the alternative functions performed by the characteristic technologies of the AI field.

Understanding the complete impacts of this distinction needs eluding from the reductionist problematic that perpetuates to haunt philosophical discussions of neurosciences aspirations as a mode of inquiry

The early prospect, which will help to build machines possessing intelligence of humans, found inspiritment in three main directions.

Firstly, proof that the functioning of the human brain and nervous system, while astonishingly perplexed from a biological perspective, is predicated on elementary all-or-nothing procedures of the type that can facilely be copied by digital electronic circuits.

Secondly, the growth of symbolic logic and formal languages that are able to communicate immense components of higher mathematics, recommending that all human reasoning might be ultimately abbreviated to similar manipulating strings of symbols according to sets of rules. Such formal operations can probably easily be imitated by a digital computer.

Thirdly, the outlook of creating faster electronic calculating devices. With regard to this, developments since the 1950s have rarely been saddening. The density of switching elements of todays microchips surpasses that of neurons in the brain.

Artificial intelligence makes industrial machines and equipment precise, credible and self-healing, making strides calibrated performance imitating human action. AI incorporates robotic controls, vision-based sensing, and geospatial systems in order to automate advanced frameworks. It improves disease detection and prevention along with its treatment, amplifies engineering systems and handles self-organizing supply chains.

We, humans, are dependent on machines for decision-making for various processes like underwriting, recruitment, fraud detection, maintenance, etc. Real Core Energy deploys machine learning that assesses production as well as performance factors to better conduct oil drilling operations and investment decisions.

Though artificial intelligence has become indispensable in almost all fields today, the presiding approaches to artificial intelligence are based in false conceptions about the nature of the mind and of the brain as a biological organ.

Sadly, the superficial models of the brain and mind, which were the initial Kickstarter of artificial intelligence, have now become the paradigm for everything called cognitive science, as well as a huge part of neurobiology. It has become a standard protocol to levy methods, concepts, models and vocabulary from the domain of artificial intelligence, computer science onto the research of the brain and the mind. It is difficult to discover a scientific paper on these subjects which does not contain terms like computing, processing, circuits, storage and retrieval of information, encoding decoding etc.

Computational neuroscience connects human intelligence and artificial intelligence by developing theoretical models of the human brain for multiple studies on its functions, including vision, motion, sensory control, and learning.

Studies in human cognition are uncovering a deeper comprehension of our nervous system and its compound processing abilities. Models that provide high-level insights into memory, data processing, and speech/object recognition are simultaneously reshaping AI.

The integration of human intelligence with artificial intelligence will evolve computers into superhumans or humanoids that go far beyond human abilities. However, it needs computing models that combine visual and natural language processing, just how the brain functions, for comprehensive communication.

Neuroscience has made significant contributions to strengthen AI research and gain its increasingly important relevance. In planning for the future amalgamation of the two fields, it is essential to value that the past contributions of neuroscience to AI have hardly consisted of a simple shift of complete solutions which can be simply re-implemented in machines. Rather, neuroscience has often been useful in a precise way, facilitating algorithmic-level questions about qualities of animal learning and intelligence of interest to AI researchers and offering initial drives toward applicable mechanisms.

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The Amalgamation of Human Brain and Artificial Intelligence - Analytics Insight

PALO ALTO, Calif., July 6, 2020 /PRNewswire/ --DeepMap today announced it has been named to the Forbes AI 50, a list of the top private companies using artificial intelligence to transform industries. DeepMap develops scalable, high-integrity mapping solutions for autonomous driving.

DeepMap, Inc. (PRNewsfoto/DeepMap)

"We are honored to be included on the Forbes AI 50 list and recognized as a technology innovator," said Mark Wheeler, Co-Founder and CTO, DeepMap. "High-definition, centimeter-level precision maps help define the world in terms that a self-driving vehicle can understand. Our technology provides a critical piece of the puzzle for safe autonomy, including Level 2+, a category of human-driven vehicles that is a step up from today's advanced driver-assistance systems."

Forbes partnered with venture firms Sequoia Capital and Meritech Capital to create the AI 50, a list of private, U.S.-based companies using artificial intelligence in meaningful business-oriented ways. To be included, companies had to be privately-held and focused on techniques like machine learning, natural language processing, or computer vision.

The Forbes announcement noted that "self-driving tech startups remain hot; the seven autonomous vehicle companies on this year's list have raised over $3 billion in total venture capital." Other autonomy companies on the list include Aurora, Embark, Ghost, Nuro, Pony.ai, and TuSimple.

About DeepMapDeepMap is accelerating safe autonomy by providing the world's best autonomous mapping and localization solutions. DeepMap delivers the technology necessary for self-driving vehicles to navigate in a complex and unpredictable environment. The company addresses three important elements: precise high-definition (HD) mapping, ultra-accurate real-time localization, and the serving infrastructure to support massive global scaling. The company was founded in 2016 and is headquartered in Palo Alto, Calif., with offices in Beijing and Guangzhou, China. Investors include Andreessen Horowitz, Accel, GSR Ventures, Generation, Goldman Sachs, NVIDIA, and Robert Bosch Venture Capital. For more information, see http://www.deepmap.ai.

Contact info: media@deepmap.ai

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DeepMap Named to Forbes AI 50 List of Most Promising Artificial Intelligence Companies - Yahoo Finance

Arnold Schwarzeneggers Terminator character is a top example of artificial intelligence, according to nearly one-in-five confused Britons.

Some 19% believed Arnie's cyborg assassin from the 1984 blockbuster film was a prime illustration of the technology.

The revelation stands in stark contrast to Prime Minister Boris Johnson's claim in a speech last week that Britain could lead the world in AI.

A survey carried out into people's understanding of artificial intelligence lays bare how much work remains to be done.

In the hit I'll be back science-fiction movie, the T-800 Terminator is sent back in time from 2029 to 1984 to kill Sarah Connor, played by Linda Hamilton.

Her son will one day become a saviour against machines in a post-apocalyptic future and needs to be destroyed.

AI pioneer Yoshua Bengio told the BBC in October 2019 he was not a fan of the Terminator films.

"They paint a picture which is really not coherent with the current understanding of how AI systems are built today and in the foreseeable future," he said.

"We are very far from super-intelligent AI systems and there may even be fundamental obstacles to get much beyond human intelligence."

But for 19% Britons, the film is a chilling demonstration of what AI can offer.

The reality is more useful predictive texting on mobile phones uses AI, as do apps like Uber and Google Maps.

However, just 41% of people questioned believed they had encountered AI in the past three months.

Researchers uncovered big gender gaps, with 69% of women saying they did not know when they last encountered AI if they ever had.

Some 51% of men thought they had used it in the past 12 weeks.

The online Populus study of 1,093 adults was carried out for communications agency Zinc Network.

Executive director Louis Brooke said: The Government has laid out an ambitious agenda for AI, seeking to turn the UK into a world leader in this area.

AI will play a vital role in helping the UK exit lockdown and overhauling health, education, travel and the workplace.

"Yet this data shows public understanding of AI is chronically low, particularly amongst women.

"For the public to buy into new uses for AI technologies, it will be vital to ensure that innovations are well understood, and benefit those who may be the most sceptical.

Some of those quizzed readily understood the technology, saying they thought it included any sort of robot that can react to its surroundings and doesn't need programming and chat bots used by companies to deal with customer service queries.

But others were more fearful of AI's potential to oust humans from the workplace.

One described it as work done by machines replacing humans and another as creepy Japanese humanoids.

Others totally missed the point, according to researchers, with responses including artificial insemination, as with cows and other animals for breeding and aliens.

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Artificial intelligence isn't all about the Terminator, tech sceptics are warned - Mirror Online

Machine learning (ML) and artificial intelligence (AI) are frequently imagined to be the gateways to a futuristic world in which robots interact with us like people and computers can become smarter than humans in every way. But of course, machine learning is already being employed in millions of applications around the worldand its already starting to shape how we live and work, often in ways that go unseen. And while these technologies have been likened to destructive bots or blamed for artificial panic-induction, they are helping in vast ways from software to biotech.

Some of the sexier applications of machine learning are in emerging technologies like self-driving cars; thanks to ML, automated driving software can not only self-improve through millions of simulations, it can also adapt on the fly if faced with new circumstances while driving. But ML is possibly even more important in fields like software testing, which are universally employed and used for millions of other technologies.

So how exactly does machine learning affect the world of software development and testing, and what does the future of these interactions look like?

A Briefer on Machine Learning and Artificial Intelligence

First, lets explain the difference between ML and AI, since these technologies are related, but often confused with each other. Machine learning refers to a system of algorithms that are designed to help a computer improve automatically through the course of experience. In other words, through machine learning, a function (like facial recognition, or driving, or speech-to-text) can get better and better through ongoing testing and refinement; to the outside observer, the system looks like its learning.

AI is considered an intelligence demonstrated by a machine, and it often uses ML as its foundation. Its possible to have a ML system without demonstrating AI, but its hard to have AI without ML.

The Importance of Software Testing

Now, lets take a look at software testinga crucial element of the software development process, and arguably, the most important. Software testing is designed to make sure the product is functioning as intended, and in most cases, its a process that plays out many times over the course of development, before the product is actually finished.

Through software testing, you can proactively identify bugs and other flaws before they become a real problem, and correct them. You can also evaluate a products capacity, using tests to evaluate its speed and performance under a variety of different situations. Ultimately, this results in a better, more reliable productand lower maintenance costs over the products lifetime.

Attempting to deliver a software product without complete testing would be akin to building a large structure devoid of a true foundation. In fact, it is estimated that the cost of post software delivery can 4-5x the overall cost of the project itself when proper testing has not been fully implemented. When it comes to software development, failing to test is failing to plan.

How Machine Learning Is Reshaping Software Testing

Here, we can combine the two. How is machine learning reshaping the world of software development and testing for the better?

The simple answer is that ML is already being used by software testers to automate and improve the testing process. Its typically used in combination with the agile methodology, which puts an emphasis on continuous delivery and incremental, iterative developmentrather than building an entire product all at once. Its one of the reasons, I have argued that the future of agile and scrum methodologies involve a great deal of machine learning and artificial intelligence.

Machine learning can improve software testing in many ways:

While cognitive computing holds the promise of further automating a mundane, but hugely important process, difficulties remain. We are nowhere near the level of process automation acuity required for full-blown automation. Even in todays best software testing environments, machine learning aids in batch processing bundled code-sets, allowing for testing and resolving issues with large data without the need to decouple, except in instances when errors occur. And, even when errors do occur, the structured ML will alert the user who can mark the issue for future machine or human amendments and continue its automated testing processes.

Already, ML-based software testing is improving consistency, reducing errors, saving time, and all the while, lowering costs. As it becomes more advanced, its going to reshape the field of software testing in new and even more innovative ways. But, the critical piece there is going to. While we are not yet there, we expect the next decade will continue to improve how software developers iterate toward a finished process in record time. Its only one reason the future of software development will not be nearly as custom as it once was.

Nate Nead is the CEO of SEO.co/; a full-service SEO company and DEV.co/; a custom web and software development business. For over a decade Nate had provided strategic guidance on technology and marketing solutions for some of the most well-known online brands. He and his team advise Fortune 500 and SMB clients on software, development and online marketing. Nate and his team are based in Seattle, Washington and West Palm Beach, Florida.

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How Machine Learning Will Impact the Future of Software Development and Testing - ReadWrite