Archive for the ‘Artificial Intelligence’ Category

Transactions in the Age of Artificial Intelligence: Risks and Considerations – JD Supra

Artificial Intelligence (AI) has become a major focus of, and the most valuable asset in, many technology transactions and the competition for top AI companies has never been hotter. According to CB Insights, there have been over 1,000 AI acquisitions since 2010. The COVID pandemic interrupted this trajectory, causing acquisitions to fall from 242 in 2019 to 159 in 2020. However, there are signs of a return, with over 90 acquisitions in the AI space as of June 2021 according to the latest CB Insights data. With tech giants helping drive the demand for AI, smaller AI startups are becoming increasingly attractive targets for acquisition.

AI companies have their own set of specialized risks that may not be addressed if buyers approach the transaction with their standard process. AIs reliance on data and the dynamic nature of its insights highlight the shortcomings of standard agreement language and the risks in not tailoring agreements to address AI specific issues. Sophisticated parties should consider crafting agreements specifically tailored to AI and its unique attributes and risks, which lend the parties a more accurate picture of an AI systems output and predictive capabilities, and can assist the parties in assessing and addressing the risks associated with the transaction. These risks include:

Freedom to use training data may be curtailed by contracts with third parties or other limitations regarding open source or scraped data.

Clarity around training data ownership can be complex and uncertain. Training data may be subject to ownership claims by third parties, be subject to third-party infringement claims, have been improperly obtained, or be subject to privacy issues.

To the extent that training data is subject to use limitations, a company may be restricted in a variety of ways including (i) how it commercializes and licenses the training data, (ii) the types of technology and algorithms it is permitted to develop with the training data and (iii) the purposes to which its technology and algorithms may be applied.

Standard representations on ownership of IP and IP improvements may be insufficient when applied to AI transactions. Output data generated by algorithms and the algorithms themselves trained from supplied training data may be vulnerable to ownership claims by data providers and vendors. Further, a third-party data provider may contract that, as between the parties, it owns IP improvements, resulting in companies struggling to distinguish ownership of their algorithms prior to using such third-party data from their improved algorithms after such use, as well as their ownership and ability to use model generated output data to continue to train and improve their algorithms.

Inadequate confidentiality or exclusivity provisions may leave an AI systems training data inputs and material technologies exposed to third parties, enabling competitors to use the same data and technologies to build similar or identical models. This is particularly the case when algorithms are developed using open sourced or publicly available machine learning processes.

Additional maintenance covenants may be warranted because an algorithms competitive value may atrophy if the algorithm is not designed to permit dynamic retraining, or the user of the algorithm fails to maintain and retrain the algorithm with updated data feeds.

In addition to the above, legislative protection in the AI space has yet to fully mature, and until such time, companies should protect their IP, data, algorithms, and models, by ensuring that their transactions and agreements are specifically designed to address the unique risks presented by the use and ownership of training data, AI-based technology and any output data generated by such technology.

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Transactions in the Age of Artificial Intelligence: Risks and Considerations - JD Supra

AI in Robotics: Robotics and Artificial Intelligence 2021 – Datamation

Artificial intelligence (AI) is driving the robotics market into various areas, including mobile robots on the factory floor, robots that can do a large number of tasks rather than being specialized on one, and robots that can stay in control of inventory levels as well as fetching orders for delivery.

Such advanced functionality has raised the complexity of robotics. Hence the need for AI.

Artificial intelligence provides the ability to monitor many parameters in real-time and make decisions. For example, in an inventory robot, the machine has to be able to know its own location, the location of all stock, know stock levels, work out the sequence to go and retrieve items for orders, know the location of other robots on the floor, be able to navigate around the site, know when a human is near and change course, take deliveries to shipping, keep track of everything, and more.

The mobile robot also has to interoperate with various shop floor systems, computer numerical control (CNC) equipment, and other industrial systems. AI helps all those disparate systems work together seamlessly by being able to process their various inputs in real-time and coordinate action.

The autonomous robotic market alone is worth around $103 billion this year, according to Rob Enderle, an analyst at Enderle Group. He predicts that it will more than double by 2025 to $210 billion.

It will only go vertical from there, Enderle said.

Thats only one portion of the market. Another hot area is robotic process automation (RPA). It, too, is being integrated with AI to deal with high-volume, repeatable tasks. By handing these tasks over to robots, labor costs are reduced, workflows can be streamlined, and assembly processes are accelerated. Software can be written, for example, to take care of routine queries, calculations, and record keeping.

Historically, two different teams were needed: one for robotics and another for factory automation. The robotics team consists of specialized technicians with their own programming language to deal with the complex kinematics of multi-axis robots. Factory automation engineers, on the other hand, use programmable logic controllers (PLCs) and shop floor systems that utilize different programming languages. But software is now on the market that brings these two worlds together.

Further, better software and more sophisticated hardware has opened the door to a whole new breed of robot. While basic models operate on two axes, the latest breed of robotic machine with AI is capable of movement on six axes. They can be programmed to either carry out one task, over and over with high accuracy and speed, or execute complex tasks, such as coating or machining intricate components.

See more: Artificial Intelligence Market

Hondas ASIMO has become something of a celebrity. This advanced humanoid robot has been programmed to walk like a human, maintain balance, and do backflips.

But now AI is being used to advance its capabilities with an eventual view toward autonomous motion.

The difficulty is no longer building the robot but training it to deal with unstructured environments, like roads, open areas, and building interiors, Enderle said.They are complex systems with massive numbers of actuators and sensors to move and perceive what is around them.

Sight Machine, the developer of a manufacturing data platform, has partnered with Nissan to use AI to perform anomaly detection on 300 robots working on an automated final assembly process.

This system provides predictions and root-cause analysis for downtime.

See more: Artificial Intelligence: Current and Future Trends

Siemens and AUTOParkit have formed a partnership to bring parking into the 21st century.

Using Siemens automation controls with AI, the AUTOParkit solution provides a safe valet service without the valet.

This fully automated parking solution can achieve 2:1 efficiency over a conventional parking approach, AUTOParkit says. It reduces parking-related fuel consumption by 83% and carbon emissions by 82%.

In such a complex system, specialized vehicle-specific hardware and software work together to provide smooth and seamless parking experience that is far faster than traditional parking. Siemens controls use AI to pull it all together.

Kawasaki has a large offering of robots that are primarily used in fixed installations. But now it is working on robotic mobility and that takes AI.

For stationary robots to work seamlessly with mobile robots, it is essential that they can exchange information accurately and without failure, said Samir Patel, senior director of robotics engineering, Kawasaki Robotics USA.

To meet such integration requirements, Kawasaki robot controllers offer numerous options, including EtherNet TCP/IP, EtherNet IP, EtherCat, PROFIBUS, PROFINET and DeviceNet. These options not only allow our robots to communicate with mobile robots, but also allow communication to supervisory servers, PLCs, vision systems, sensors, and other devices.

With so many data sources to communicate with and instantaneous response needed to provide operational efficiency and maintain safety, AI is needed.

Over time, each robot accumulates data, such as joint load, speed, temperature, and cycle count, which periodically gets transferred to the network server, Patel said. In turn. the server running an application, such as Kawasakis Trend Manager, can analyze the data for performance and failure prediction.

Sight Machine, in close cooperation with Komatsu, has developed a system that can rapidly analyze 500 million data points from 600 welding robots.

The AI-based system can provide early warning of potential downtime and other welding faults.

See more: Top Performing Artificial Intelligence Companies

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AI in Robotics: Robotics and Artificial Intelligence 2021 - Datamation

Artificial intelligence: ‘The window to act is closing fast’ – The Irish Times

Artificial intelligence (AI) is a force for good that could play a huge part in solving problems such as climate change. Left unchecked, however, it could undermine democracy, lead to massive social problems and be harnessed for chilling military or terrorist attacks.

Thats the view of Martin Ford, futurist and author of Rule of the Robots, his follow-up to Rise of the Robots, the 2015 New York Times bestseller and winner of the Financial Times/McKinsey Business Book of the Year, which focused on how AI would destroy jobs.

In the new book, Ford, a sci-fi fan, presents two broad movie-based scenarios.

The first is a world based on Star Trek values, where Earths problems have been solved. Technology has created material abundance, eliminated poverty, cured most disease and addressed environmental issues. The absence of traditional jobs has not led to idleness or lack of dignity as highly educated citizens pursue rewarding challenges.

The alternative dystopian future is more akin to The Matrix, where humanity is unknowingly trapped inside a simulated reality.

The more dystopian outcome is the default, if we dont intervene. I can see massive increases in inequality and various forms of entertainment and recreation such as video gaming, virtual reality and drugs becoming attractive to a part of the population that has been left behind, he tells The Irish Times.

Fords extensive research for both books involved talking to a wide cross-section of those working on the frontiers of artificial intelligence. While the unpredicted Covid-pandemic punctuated the intervening years, most of what he wrote in 2015 has been amplified, he feels. Covid if anything has acted as an accelerant for AI and robotics, with enduring effects in areas such as remote working, social distance and hygiene.

On the positive side, AI has led to huge medical advances including the recent rapid Covid vaccine development and deployment. With the pace of innovation slowing in other areas, AI is potentially a game changer in areas such as the climate crisis, he says.

Worries of the bad effects of AI, however, permeate his thoughtful new volume on the subject.

Employment is one.

Virtually any job that is fundamentally routine or predicable in other words nearly any role where workers face similar challenges again and again has the potential to be automated in full or in part.

Studies suggest that as much as half of the US workforce is engaged in such work and that tens of millions of jobs could evaporate in the US alone. This wont just affect lower-skilled, low-wage workers, he warns. Predictable intellectual work is at especially high risk of automation because it can be performed by software, whereas manual labour, in contrast, requires a more expensive robot.

Ford is generally pessimistic that workers will be able to move up the value chain or move to areas less affected by the rise of AI. Some will, he acknowledges, but he wonders will truck drivers, for example, become robotics engineers or personal care assistants.

Moreover, many of the new opportunities being created are in the gig economy where workers typically have unpredictable hours and incomes, all of which points to rising inequality and dehumanising conditions for a large section of the workforce.

Surveillance is another issue of concern. He highlights the case of the use of an app developed by the firm Clearview AI in the US.

In February 2019, the Indiana State Police were investigating a case where two men got into a fight in a park, one pulled a gun, shot the other man and fled the scene. A witness had filmed the incident on a mobile phone and the police uploaded the images to a new facial-recognition system they had been experimenting with.

It generated an immediate match. The shooter had appeared in a social media video with a description that included his name. It took just 20 minutes to solve the crime even though the suspect had not been previously arrested and did not hold a drivers licence. When this was revealed along with other information about the firm, it ignited major data-privacy concerns.

Data privacy is one thing but the capacity for AI to generate deepfakes takes this to another level. Ford offers up a scenario in which a politicians voice could be imitated in the run-up to an election, planting comments that would deliberately damage their reputation. Spread virally on social media, it might be hard to undo the stickiness of this. How many people would hear the denial or choose to believe the fake was not authentic?

A sufficiently credible deepfake could literally shape the arc of history and the means to create such fabrications might soon be in the hands of political operatives, foreign governments or even mischievous teenager, he says. In the age of viral videos, social media shaming and cancel culture, virtually anyone could be targeted and have their careers and personal lives destroyed.

Because of its history of racial injustice, the US may be especially vulnerable to orchestrated social and political disruption, he observes. Weve seen how viral videos depicting police brutality can almost instantly lead to widespread protests and social unrest. It is by no means inconceivable that, at some point in the future, a video so inflammatory that it threatens to rend the very social fabric could be synthesised perhaps by a foreign intelligence agency.

There are smart people working on solutions. Sensity, for example, markets software it claims can detect most deepfakes but inevitably there will be an arms race between the poachers and gamekeepers. He likens this to the race between computer virus creators and those who sell cybersecurity solutions, one in which malicious actors tend to maintain a continuous edge.

An example of the difficulties in this area was highlighted by an experiment that found that simply adding four small rectangular black and white stickers to a stop sign tricked an image recognition system of the type used in self-driving cars into believing it was instead a 45mph speed-limit sign. A human observer might not even notice and certainly wouldnt be confused but AIs error could have fatal consequences.

Ford paints an even more terrifying scenario of lethal autonomous weapons. Consider the possibility of hundreds of autonomous drones swarming the US Capitol building in a co-ordinated attack. Using facial recognition technology, they seek and locate specific politicians, carrying out multiple targeted assassinations. This chilling vision was sketched out in a 2017 short film called Slaughterbots, produced by a team working under the direction of Stuart Russell, professor of computer science at the University of California, Berkeley, who has focused much of his recent work on the risks of AI.

This disturbing vision is quite realistic, he believes.

My own view is rather pessimistic. It seems to be that the competitive dynamic and lack of trust between major countries will probably make at least the development of fully autonomous weapons a near certainty. Every branch of the US military as well as nations including Russia, China, the United Kingdom and South Korea are actively developing drones with the ability to swarm.

Low barriers to entry mean that even small, under-resourced groups could also gain access to this type of warfare. Commercial drones could be easily modified, he explains. We have to worry about what human beings will choose to do with weapons that are no more intelligent than an iPhone, but which are ruthlessly competent at identifying, tracking and killing targets.

This is a near-term rather than long-term worry, he adds, and the window to act is closing fast.

AI needs to be the subject of regulation, he maintains, not by politicians in Congress or elsewhere but by specialist authorities in the same ways that financial markets are regulated.

Ford also worries about China and devotes a large section of the book to what he views as an AI arms race between China and the West. As well as concerns about privacy for its citizens and human rights for oppressed minorities, he worries about the capacity of China to export, not alone its all-pervasive AI technology to other regions but also its world view, which is very much at odds with western values.

Its going to become more Orwellian. To live in China will be to have every aspect of your life tracked. Maybe it will be like boiling a frog and people will not notice or care, but we certainly dont want that here [in the West].

One possible silver lining for Chinas citizens, he concedes, however, is that crime rates collapse with AI-based surveillance. Thats a trade-off that might just be worth considering.

Rule of the Robots: How artificial intelligence will transform everything, by Martin Ford, is published by Basic Books, New York.

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Artificial intelligence: 'The window to act is closing fast' - The Irish Times

Alation Acquires Artificial Intelligence Vendor Lyngo Analytics – Business Wire

REDWOOD CITY, Calif.--(BUSINESS WIRE)--Alation Inc., the leader in enterprise data intelligence solutions, today announced the acquisition of Lyngo Analytics, a Los Altos, Calif.-based data insights company. The acquisition will elevate the business user experience within the data catalog, scale data intelligence, and help organizations drive data culture. Lyngo Analytics CEO and co-founder Jennifer Wu and CTO and co-founder Joachim Rahmfeld will join the company.

Lyngo Analytics uses a natural language interface to empower users to discover data and insights by asking questions using simple, familiar business terms. Alation offers the most intelligent and user-friendly machine-learning data catalog on the market. And by integrating Lyngo Analytics artificial intelligence (AI) and machine-learning (ML) technology into its platform, Alation deepens its support for the non-technical user, converting natural language questions into SQL.

The integration lowers the barrier to entry for business users. Now, they can acquire and develop data-driven insights from across an enterprise's broad range of data sources. This means even data consumers without SQL expertise can ask questions in natural language and find data and insights without the support of data analysts. The acquisition will help organizations drive data culture by putting data and analytics into the hands of the masses.

Wu will join Alation as Senior Director of Product Management, where she will be responsible for product strategy and delivery for natural language data search, discovery, and exploration experiences. Rahmfeld, who is also a part-time, graduate-level deep learning and natural language processing lecturer at UC Berkeleys Master of Information and Data Science Program, will be Senior Director of AI/ML Research. He will be responsible for Alations AI and machine learning center of excellence, building both platform and application experiences that leverage AI and ML to enhance Alations value for business and technical users.

Alation created the first machine learning data catalog and were known for providing the most user-friendly interface on the market, said Raj Gossain, Chief Product Officer, Alation. With this acquisition, were building on the best. Were doubling down on key aspects of the platform that will help drive data culture and spur innovation and growth. Jennifer and Joachim developed a unique solution for a complex data and analytics issue, and Im excited to welcome them to the Alation team.

The acquisition is the latest milestone for Alation, which announced a $110 million Series D funding round and a $1.2 billion market valuation in June 2021. Alation is growing quickly, earning the trust of nearly 300 customers, including leading global brands such as Cisco, Exelon, GE Aviation, Munich Re, NASDAQ, and Pfizer. The company has more than 450 employees globally and is hiring. Recently, Alation was named a leader in The Forrester Wave: Data Governance Solutions, Q3 2021 report and Snowflakes Data Governance Partner of the Year.

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About Alation

Alation is the leader in enterprise data intelligence solutions including data search & discovery, data governance, data stewardship, analytics, and digital transformation. Alations initial offering dominates the data catalog market. Thanks to its powerful Behavioral Analysis Engine, inbuilt collaboration capabilities, and open interfaces, Alation combines machine learning with human insight to successfully tackle even the most demanding challenges in data and metadata management. Nearly 300 enterprises drive data culture, improve decision making, and realize business outcomes with Alation including AbbVie, American Family Insurance, Cisco, Exelon, Fifth Third Bank, Finnair, Munich Re, NASDAQ, New Balance, Parexel, Pfizer, US Foods and Vistaprint. Headquartered in Silicon Valley, Alation was named to Inc. Magazines Best Workplaces list and is backed by leading venture capitalists including Blackstone, Costanoa, Data Collective, Dell Technologies, Icon, ISAI Cap, Riverwood, Salesforce, Sanabil, Sapphire, and Snowflake Ventures. For more information, visit alation.com.

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Alation Acquires Artificial Intelligence Vendor Lyngo Analytics - Business Wire

Why Artificial Intelligence Research Needs More Women – Women Love Tech

Its likely no surprise that women are still massively underrepresented in the tech industry today. Even with a push over the last few years for more women to pursue careers in STEMscience, technology, engineering, and mathematicsthey still make up a tiny percentage of those working in the field. Data shows that of those doing STEM-focused research around the world, less than 30% are women.

Unfortunately, when you narrow the focus down to women working specifically in smart tech and machine learning, the numbers get even smaller. You might wonder why it matters who is behind the data and code creation when its essentially a non-gendered machine or robot doing all of the processing, but it does. Machines arent inherently biased, but humans are, and when humans are teaching machines how to learn and what to do, our biases naturally become part of the code.

Our computers, phones, and any other smart devices that we use today utilize technology that mimics our thought and decision-making processes. So if the majority of people working with smart tech such as artificial intelligence are men, then anything that utilizes AI will skew towards the male perspective.

Though artificial intelligence might bring to mind images of a future world run by robot overlords, it is far less ominous and science fiction-like than that. AI is already a part of so many things that we use and interact with dailyits not the futureits the present.

Though artificial intelligence sounds like a far-fetched term, it is basically the use of algorithms in computer systems to mimic how humans process information, and the more input it receives, the smarter it gets. Its not just a part of our personal devices either; businesses are using AI to improve customer service and interpret data to further develop their systems and run more efficiently.

Google is even leveraging the power of AI to create tools to enhance healthcare and help conservationists and scientists save endangered species and preserve indigenous languages. There is no end to the way we can harness the usefulness of AI. We can apply it to numerous situations to advance our capabilities and solve complex real-world problems.

Artificial intelligence and deep learning systems are not just likely to change our future; it is already quite evident that they will and are already doing so. As humans, we are limited by our own minds and capabilities. There is only so much we can dobut AI and deep learning machines will allow us to scale our potential and complete tasks, projects, and missions that we would otherwise not be able to do.

Deep learning refers to systems with more advanced neural networks that can actually draw conclusions from the input it receives and adapt to changes. In contrast, some more basic and earlier forms of AI machines can only do what they are specifically taught to do.

Some examples of deep learning applications include:

So how will deep learning and other advanced forms of AI affect our future? The simple answer is that it will make everything easier and more efficient. As mentioned above, as humans, we can only do so much. We should continue to maintain a human presence and interactions within our business operationspeople still need that human touch. But when it comes to interpreting data and solving complex equations to optimise our systems and make new advancements, we need the help of AI.

Several industries can benefit from having more women, but especially those specifically handling the development and research of machine learning systems. AI is already an integral part of our society and nearly all industries. Our economy and its infrastructure run on many systems that use AI every day. The problem is, when these systems are all designed primarily by men, the processes that the computers use to learn and interpret data will result in a skewed outcome.

Mark Minevich, a contributor to Forbes.com, writes, organisations will always fail to harness the fullest capacity of their digital innovations without including women, as machine learning technologies will be fed a constant stream of biased data, producing junk results that are not reflective of the full picture, causing potentially catastrophic harm to organisations. And hes right. If AI is becoming a significant part of our society and infrastructure, and women will undoubtedly continue to be a part of this society, we need to include them in the research and development of these systems.

From transportation and education to media, customer service, and healthcare and wellnessindustries are increasingly integrating artificial intelligence into their systems. Without more representation of women, the data these industries work off of and use to improve their operations will be deeply inaccurate. We dont just need more women researching AI; we must have them. Continuing to leave them out is not an option. It is vital to the growth and success of AI itself and our growth as a society, and our ability to advance.

Report by Beau Peters.

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Why Artificial Intelligence Research Needs More Women - Women Love Tech