Artificial intelligence (AI) technology has exploded in recent years. Common AI personal assistants such as Siri, Alexa, and Google Assistant are helping people figure out their daily schedules, controlling the lights and thermostats in homes, and helping commuters find the best route to work. And while these recent advancements are amazing, theyre really only a sneak preview of how AI can and will impact society as a whole.

Sales efficiency is an area ripe for AI assistance AI is at the point where it can be just as useful, if not more useful, in the office than at home on ones handheld device. Business AI has evolved from being a sophisticated calculator or database analyzer to an entity that can tell businesses solve their biggest challenges and enhance their market offerings. Although more organizations spanning industries are tuning into the potential of AI, many are still not capitalizing on its potential to transform their sales practices.

For many organizations, consistent lead generation is elusive. Despite the increase in tools, technology, and access to customers, companies still struggle to speak to the right target customers with the right message at the right time. Often, lead generation feels like throwing darts at a board and hoping one will hit the target.

The best lead gen tools will always be people; having the opportunity to spend one on one time with targets, listening to their goals and frustrations is a dream for every sales person. But its not realistic. Luckily, organizations like LeadCrunch are enhancing AI-fueled lead-gen platforms that enable organizations to understand customers on an individual basis so that they can eventually connect with them, person-person throughout the sales cycle. Providing a holistic B2B demand generation solution, it uses an effective combination of AI technology and human verification to engage the best targets for your customer base.

Because AI has such a wide reach, companies can use it to gain and vet more leads, which will ultimately result in more possibilities for contact and upselling while minimizing the need for a large sales team. The Harvard Business Review recently found that when Epson implemented an AI sales assistant, their lead response rate increased by 240%. Furthermore, as the technology continues to improve, companies can scale back or reprioritize its human sales component without the risk of losing potential clients.

In addition to initial contact and lead generation, AI is useful for assisting sales associates with administrative and routine tasks. Rather than having your human sales team be bogged down with paperwork, initial contacts, sorting, scheduling, and other administrative duties, AI is able to complete these quickly and with a high level of accuracy. Administrative tasks dont necessarily need a human component and, as such, are well within the capabilities of modern AI.

Without administrative tasks, a salesperson now has more time to pursue leads sent to them by AI so they can begin to establish a human relationship which is often needed to eventually get the sale. In a recent study, the average American employee spends 40% of their working hours on administrative tasks. With more time to focus on building relationships with clients, sales associates can work with even more clients at once, making the entire process much more efficient.

Moreover, AI can coach your sales force as well. Rather than having your sales team focus on administrative duties and analytics, companies can let AI figure out how and why a particular sales person is struggling. By identifying potential weaknesses, sales associates can rely on AI to prepare them and coach them for future interactions.

The beauty of an AI_driven platform, like LeadCrunchs is that it has the power to adapt and apply new customer insights to future initiatives. Customer behaviors and marketing engagements change on a day-to-day basis, and until now, the audience data that companies rely on has failed to keep up. The implementation of Artificial Intelligence enables LeadCrunch to provide its clients consistnetly evolving data that allows them to grow with their target customers, rather than play catch-up.

AI is great for generating leads and for performing routine jobs to help the sales team focus on sales, but its also useful for managing an already-existing customer base as well. For companies with large user bases that offer renewable subscription services or upgrades, AI can help manage a system to both pitch and sell those subscriptions, renewals, or upgrades. After all, the probability of selling more to an existing client is 60% 70%

This is especially useful for Internet companies that want to keep only a few individuals on hand but still be able to manage a large number of clients. For example, rather than having a team of sales representatives standing by to field questions, AI can field and respond to most of these questions instantly. Furthermore, for customer service issues, having an AI platform act as a gatekeeper will facilitate quick responses to frequently asked questions while letting human customer service agents deal with more complicated issues. With AI, a company can meet the needs of an ever-expanding clientele base without spending large amounts of money.

From generating leads, to assisting with administrative tasks, to maintaining and upselling current customers, LeadCrunchs AI lead generation platform is the ultimate sales hack. Its a unique tool that will give a company a much broader reach without any of the traditional overhead, a once unimaginable concept.

What are some other ways AI can help your company with sales?

This post is part of our contributor series. The views expressed are the author's own and not necessarily shared by TNW.

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Why AI is the ultimate sales hack - TNW

Micron Technology said it acquired Fwdnxt, a maker of hardware and software tools for artificial intelligence deep learning applications.

When combined with Microns memory chips, Fwdnxt (pronounced forward next) will enable Micron to explore deep learning solutions required for data analytics, particularly with internet of things and edge computing.

Micron also announced a series of flash-based solid-state drives for consumers and enterprises, as well as new security products. The Boise-based company unveiled them at its Micron Insight event in San Francisco.

Sanjay Mehrota, CEO of Micron, said at the event that such solutions are unlocking data insights, with memory and AI at the heart of it.

Our vision is transforming the way the world uses information to change life, Mehrota said. The compute architectures of yesterday are not suitable for tomorrow.

With the acquisition, Micron is integrating compute, memory, tools and software into an AI development platform. This platform, in turn, provides the building blocks required to explore innovative memory optimized for AI workloads.

Above: Sanjay Mehrota, CEO of Micron, at Micron Insights.

Image Credit: Dean Takahashi

Fwdnxt is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility, said Micron executive vice president and chief business officer Sumit Sadana, in a statement. Fwdnxts five generations of machine learning inference engine development and neural network algorithms, combined with Microns deep memory expertise, unlocks new power and performance capabilities to enable innovation for the most complex and demanding edge applications.

Fwdnxt provides efficient and high-performance hardware and software solutions based on deep learning and neural networks. As companies develop more complex AI and machine learning systems, the hardware used to train and run those models becomes increasingly important.

The Micron Deep Learning Accelerator (DLA) technology, powered by the AI inference engine from Fwdnxt, gives Micron the tools to observe, assess, and ultimately develop innovation that brings memory and computing closer together, resulting in higher performance and lower power demands.

Microns DLA technology provides a software-programmable platform that supports a broad range of machine learning frameworks and neural networks and allows for processing vast amounts of data quickly in an easy-to-use interface.

This feels like Micron will be competing with Intel and Nvidia. That may be true, but it will also likely continue to partner with them. Still, Micron is moving deeper into the ecosystem to solve problems for its customers.

In the long run, we think compute is best done in memory, Mehrota said.

Jon Peddie, analyst at Jon Peddie Research, said in an email, Micron with its acquisition of Fwdnxt extends its platform stack into apps, software development kits, and software tools as other semi companies like Xilinx, Qualcomm, Texas Instruments, and others. That raises the value add and increases margins as they get closer to the end customer.

Micron said its DLA can consume massive amounts of data and then return insights that launch discoveries. For example, Micron is collaborating with doctors and researchers at Oregon Health & Science University to use convolutional neural networks (CNNs) running on DLA to process and analyze 3D electron microscopy images. The goal of this collaboration is to discover new insights for treating cancer. Micron is also partnering with physicists at leading nuclear research organizations who are experimenting with DLA-based CNNs to classify the results of high energy-particle collisions in near real time and detect rare particle interactions that are believed to occur in nature.

Micron also introduced its solid-state drives, including the Micron 5300, which uses cost-effective 96-layer 3D TLC NAND flash memory chips. The 5300 series enables strong performance for read-intensive and mixed-use segments, including media streaming, BI/DSS, OLTP, and block and object storage. It has 50% higher reliability than the past.

Above: Micron 7300 SSDs

Image Credit: Micron

The Micron 7300 Series SSD makes non-volatile memory technology more accessible, enabling mainstream performance in datacenters for virtualized and I/O-sensitive workloads. The 7300 SSD lineup produces high read throughput with low latency. Micron offers 14 different 7300 models with capacity points from 400GB to 8TB. It is available in December.

Above: Crucials new portable SSD.

Image Credit: Dean Takahashi

With the launch of Crucial X8, Micron is moving into consumer portable SSDs. The product focuses on fast transfer times and high capacity for consumers storing photos, videos, and documents, as well as curating music, game libraries, and video collections.

Mehrota said that half of gamers use a terabyte of storage on their devices. Crucial X8 is immediately available for ordering in capacities up to 1TB.

With read speeds up to 1,050MB/s,the drive performs 1.8 times faster than similar portable SSDs within the same price category and up to 7.5 times faster than portable hard drives. The Crucial X8 is compatible with a variety of devices, including PCs, Macs, PS4s, XBOX Ones, iPad Pros, Chromebooks and select Android devices.

The Authenta Key Management Service is Microns new security technology. It is a fully integrated and provisioned secure element function, offered on standard flash chips. Authenta Key Management Service secures data at the edge to protect the 75 billion devices expected to be online by 2025.

By integrating this product through flash, it can be utilized for hardware-based system protection and to establish strong device identities for internet of things devices, creating instant access to security-as-a-service capabilities. The Authenta Key Management Service is backed by trusted silicon credentials, without having to leverage hardware/SoC changes. Sadana said that Micron can embed the security in the chip while it is still inside the chip factory.

Mehrota said that the company has instrumented its factories with sensors for smart manufacturing, with tens of millions of measurement points. The efficiencies from that have improved output by 10% and time to yield by 25%, he said.

Micron also announced the worlds fastst SSD, using 3D XPoint technology. The Micron X100 SSD is the first solution in a family of products from Micron targeting storage- and memory-intensive applications for the data center.

These solutions will leverage the strengths of 3D XPoint technology and usher in a new tier in the memory-to-storage hierarchy with higher capacity and persistence than DRAM, along with higher endurance and performance than NAND flash memory.

The SSD offers up to 2.5 million input/output operations per second (IOPs), more than three times faster than todays competitive SSD offerings. It also has more than 9GB/s bandwidth in read, write and mixed modes and is up to three times faster than todays competitive NAND offerings. And it provides consistent read-write latency that is 11 times better than NAND SSDs.

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Micron Technology acquires Fwdnxt to move into AI hardware and software - VentureBeat

The COVID-19 pandemic is accelerating the digital transition and the adoption of artificial intelligence (AI) tools and Internet of Things (IoT) devices in many areas of society. While there has been significant focus on leveraging this technology to fight the pandemic, the technology also will have broader and longer-term benefits. As the New York Times has explained, social-distancing directives, which are likely to continue in some form after the crisis subsides, could prompt more industries to accelerate their use of automation.

For businesses proceeding with reopenings over the coming weeks and months, and for sectors that have continued to operate, AI and IoT technologies can greatly improve the way they manage their operations, safely engage with customers, and protect employees during the COVID-19 crisis and beyond. But businesses also should take steps to ensure that their use of AI and IoT technologies complies with the evolving legal requirements that can vary based on several factors, including the industry sector where the technology is deployed and the jurisdiction where it is used. Businesses also will want to have mechanisms in place to help ensure that the technology is used appropriately, including appropriate oversight and workforce training and other measures.

How Businesses are Using AI and IoT in Response to the COVID-19 Crisis

Businesses may want to leverage AI and IoT technologies to support their operations in a number of ways. In connection with reopenings, as discussed previously in this series, many businesses will deploy AI and IoT technologies to protect the health and safety of their employees. New and existing tools are being used for health monitoring, remote interactions, and contactless access and security. For example, some businesses are instituting return-to-workplace policies that require temperature scanning or other monitoring of health indicators through wearable AI (i.e., wearable IoT devices powered by AI). Businesses also may use AI and IoT technologies to automate office tasks to enable some employees to continue working from home, to conduct meetings and trainings remotely, or to facilitate social distancing in the workplace by, for example, relying on facial recognition technology in lieu of front-desk security personnel.

Beyond the initial reopening steps, manufacturers and other businesses are considering how expanded use of robotics and automation can increase efficiencies, improve workforce safety, and facilitate contactless transport and deliveries. Businesses increasingly also are using AI tools to help improve logistics and supply chain management, provide input for decision making, and to support customer engagement. In some cases, AI and IoT tools are developed by companies for their own usebut increasingly businesses are turning to cloud services providers and other third parties for many of these technologies and services.

Key Questions to Ask Before Deploying AI and IoT Solutions

When businesses are implementing AI and/or IoT technologies, such as robotics, wearable devices, AI supply chain management and human resources management tools, health monitoring equipment and other AI or internet-connected products or services, they should consider the existing and proposed legal obligations and the benefits and risks that may be introduced by use of these tools. Businesses should ask, for example:

The AI/IoT Legal Landscape

Businesses must be mindful of the rapidly evolving legal landscape surrounding AI and IoT technology, and the potential need to comply with new requirements that may vary by industry or jurisdiction. On the AI front, earlier this year, the European Commission solicited comments on an artificial intelligence white paper that describes a proposed AI regulatory framework. The white paper embraces a risk-based and proportionate approach and acknowledges that high-risk AI applications should be regulated differently than lower-risk applications. Among other things, the European Commission white paper proposes a pre-market assessment requirement for high risk AI. The European Commission also has proposed changes to the AI liability laws. In the United States, President Trump signed an Executive Order on AI in February 2019, which calls for a coordinated federal AI strategy. In early 2020, the Trump Administration proposed 10 Principles for AI Regulation which takes a lighter touch approach to regulation than the European Commission white paper. Pursuant to the Executive Order, the National Institute of Standards and Technology has submitted a plan for developing AI standards. Meanwhile, in April the Federal Trade Commission provided guidance on the use of AI and algorithms in automated decision making. There also is a variety of pending AI legislation in Congress, and some state and local governments continue their efforts to examine regulation of AI. Several entities are also working to coordinate these types of efforts internationally. More information is available on our Artificial Intelligence Toolkit.

On the IoT side, while Congress is considering legislation, states are increasingly active as well. For example, both the California and Oregon IoT security laws came into effect in 2020. California is also currently considering a bill regarding trip mobility data collected from autonomous vehicles and other mobile applications. The bill would require the operators of autonomous vehicles to share aggregated and de-identified trip data for transportation planning and safety purposes, including in response to the COVID-19 pandemic. Standards continue to evolve outside the United States as well. For example, the United Kingdom has been active in providing guidance regarding the security of consumer IoT devices. In October 2018, the Department for Digital, Culture, Media and Sport, published a Code of Practice for Consumer IoT Security, and in March 2020, the National Cyber Security Centre issued guidance about using smart security cameras safely in the home.

Privacy and Security Concerns Presented by AI and IoT

Businesses should proactively manage privacy-related legal matters arising from the use of AI and IoT technologies. In order to function, many of these tools must collect significant amounts of personal data from individuals. For example, wearable AI devices that can be used for health monitoring may collect geolocation information, data about an individuals physical movements, heartrate and blood pressure indicators, biometric information such as fingerprints and facial features, and other personal information. Some of these devices also may collect and store more data than they actually use, or retain data for lengthy periods of time. These devices, when used with AI, also may create new personal data about an individual, such as the likelihood an individual may experience severe COVID-19 symptoms based on pre-existing conditions.

Any solution that involves the collection, generation or use of personal information should have adequate privacy and data security safeguards in place. Businesses should have privacy policies in place and should comply with such policies as well as applicable law. Heightened privacy requirements may apply if the information being collected is particularly sensitive in nature. For example, the collection of biometric and health information may be subject to federal laws regulating the use of medical information, such as the Health Information Portability and Accountability Act (HIPAA) or the Americans with Disabilities Act (ADA), and state laws regulating the collection of biometric information, such as the Illinois Biometric Information Privacy Act, or medical confidentiality. Similarly, automated technologies that record audio or video data could implicate state wiretap laws if that collection occurs without the consent of the individuals being recorded.

FDA Regulatory Issues

Some AI and IoT technologies are regulated by FDA. For example, digital health screening tools may ask employees about risks for exposure to COVID-19 (e.g., COVID-19 status of individuals in the employees household, COVID-19 testing history, social distancing practices) or about the individuals symptoms (such as those identified by the U.S. Centers for Disease Control and Prevention (CDC)). Some technologies may simply transmit that information to a medical professional for assessment, while other tools may utilize AI and IoT technologies to provide an assessment of the individuals exposure risk directly to the individual and/or the employer.

Depending on the functionality and intended use of the technology, it may be subject to regulation as a medical device. FDA generally does not regulate tools that match user-specific information (e.g., symptoms) to established reference information (e.g., CDC guidelines). Likewise, tools intended to help a patient document their health and communicate health information with a healthcare professional are not regulated as devices. On the other hand, FDA would likely regulate as a medical device an AI and IoT technology that provides patients recommendations on COVID-19 diagnosis if it does more than automate health authority guidelines.

While companies utilizing technologies developed by third parties will generally not be subject to FDA regulation, firms that develop their own technologies could be subject to regulation as a device manufacturer, depending on the functionality of the product. Companies developing tools, including those contracting out certain elements of the development, should carefully consider whether FDA or other regulators will actively regulate the technology.

Employee and Workforce Issues

Although some technology solutions are likely to be temporary, in many cases businesses are simply accelerating the adoption of permanent changes to how they operate. It is therefore important to consider the impact that these technologies may have on the workforce in both the immediate and long term.

Automation and robotics can protect employees from virus-related health dangers, but they may require their own safety precautions. While the Department of Labors Occupational Safety and Health Administration (OSHA) has not issued regulations on the use of robotics in the workplace, it has issued guidance explaining how existing workplace safety regulations can affect robotics and a guide for best practices regarding robotics safety. These guidelines explain how to think about worker training, hazard assessment, and safeguarding the workplace in the context of workplace robotics. In the future, workplace health and safety is an area that is likely to be subject to additional regulation relating to AI and robotics, and businesses should pay close attention to those developments as they evolve.

Employers also should be mindful of the need for workforce training. To help ensure that AI and IoT are deployed properly, employees may need to be trained on its use. For example, businesses using health monitoring systems in connection with returning to the workplace may need to train some personnel on the proper use of such systems. In addition, as businesses continue with their digital transitions, they may need to devote more employee attention to data management, and they may be better able to optimize employee time.

Of course, businesses should also keep in mind broader employment law concerns when implementing AI and IoT technology solutions, including potential issues related to bias, medical confidentiality, recordkeeping, employee monitoring, wage and hour requirements, protections for legal off-duty conduct, and labor law protections.

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Return to Workplace Considerations for Businesses Using AI and IoT Technologies - Lexology

By reverse-engineering signals sent by the brain, researchers at Carnegie Mellon University have been working on an AI that can read complex thoughts simply by looking at brain scans. Using data collected from a functional magnetic resonance imaging (fMRI) machine, the CMU scientists feed that data into their machine learning algorithms, which then locate the building blocks that the brain uses to create complex thoughts.

Impressively, the study showed that the team were able to demonstrate where and how the brain was being triggered while processing 240 complex events, covering everything from individuals to places and even various physical actions or aspects of social interaction. It's by understanding these triggers that the algorithm can use the brain scans to predict what is being thought about at the time, connecting these thoughts into a coherent sentence.

Selecting 239 of these complex sentences and feeding the AI the corresponding brain scans, the algorithm managed to successfully predict the correct thoughts with an astounding 87 percent accuracy. It could also do the reverse, receiving a sentence and then outputting an accurate image of how it predicted that thought would be mapped inside a human brain.

The astonishing research shows just how far deep learning has come. If you weren't worried about the rise of super powered machines before, now that they can read minds, it's probably time to start preparing for the inevitable robot apocalypse.

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Scientists made an AI that can read minds - Engadget

The European approach to artificial intelligence (AI) will help build a resilient Europe for the Digital Decade where people and businesses can enjoy the benefits of AI. It focuses on 2 areas: excellence in AI and trustworthy AI. The European approach to AI will ensure that any AI improvements are based on rules that safeguard the functioning of markets and the public sector, and peoples safety and fundamental rights.

To help further define its vision for AI, the European Commission developed an AI strategy to go hand in hand with the European approach to AI. The AI strategy proposed measures to streamline research, as well as policy options for AI regulation, which fed into work on the AI package.

The Commission published its AI package in April 2021, proposing new rules and actions to turn Europe into the global hub for trustworthy AI. This package consisted of:

Fostering excellence in AI will strengthen Europes potential to compete globally.

The EU will achieve this by:

The Commission and Member States agreed boost excellence in AI by joiningforces on AI policy and investment. The revised Coordinated Plan on AI outlines a vision to accelerate, act, and align priorities with the current European and global AI landscape and bring AI strategy into action.

Maximising resources and coordinating investments is a critical component of the Commissions AI strategy. Through the Digital Europe and Horizon Europe programmes, the Commission plans to invest 1 billion per year in AI. It will mobilise additional investments from the private sector and the Member States in order to reach an annual investment volume of 20 billion over the course of the digital decade.

The newly adopted Recovery and Resilience Facility makes 134 billion available for digital. This will be a game-changer, allowing Europe to amplify its ambitions and become a global leader in developing cutting-edge, trustworthy AI.

Access to high quality data is an essential factor in building high performance, robust AI systems. Initiatives such as the EU Cybersecurity Strategy, the Digital Services Act and the Digital Markets Act, and the Data Governance Act provide the right infrastructure for building such systems.

Building trustworthy AI will create a safe and innovation-friendly environment for users, developers and deployers.

The Commission has proposed 3 inter-related legal initiatives that will contribute to building trustworthy AI:

The Commission aims to address the risks generated by specific uses of AI through a set of complementary, proportionate and flexible rules. These rules will also provide Europe with a leading role in setting the global gold standard.

This framework gives AI developers, deployers and users the clarity they need by intervening only in those cases that existing national and EU legislations do not cover. The legal framework for AI proposes a clear, easy to understand approach, based on four different levels of risk: unacceptable risk, high risk, limited risk, and minimal risk.

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A European approach to artificial intelligence | Shaping ...

Undetected machine failures are the most expensive ones. That is why many manufacturing companies are looking for solutions that automate and reduce maintenance costs. Traditional vibrodiagnostic methods can be too late in many cases. Taking readings in the presence of a diagnostician occasionally may not detect a fault in advance. 2017 Position Paper from Deloitte (Deloitte Analytics Institute 7/2017) claimed that maintenance in the environment of Industry 4.0.The benefits of predictive maintenance are dependent on the industry or the specific processes that it is applied to. However, Deloitte analyses at that time have already concluded that material cost savings amount to 5 to 10% on average. Equipment uptime increases by 10 to 20%. Overall maintenance costs are reduced by 5 to 10% and maintenance planning time is even reduced by 20 to 50%! Neuron Soundware has developed a artificial intelligence powered technology for predictive maintenance.

Stories from companies that have embarked on the digital journey are no longer just science fiction. They are real examples of how companies are coping with the lack of skilled labor on the market. Usually mechanic-maintainer who regularly goes around all the machines and diagnoses their condition by listening to them. Some companies are nowlooking for new maintenance technologies to replace

A failure without early identification means replacing the entire piece of equipment or its part. Waiting for the spare part which may not be in stock right now. Because it is expensive to stock replacement equipment. Devaluation of the current pieces of the component in the production thus the discarding of the entire production run. Finally, yet importantly, it would represent up to XY hours of production downtime. The losses might run into tens of thousands of euros.

Such a critical scenario is not possible if the maintenance technology is equipped with artificial intelligence in addition to the mechanical knowledge of the machines. It applies this knowledge itself to the current state of the machine. It is also able to recognize which anomalous behavior is currently occurring on the machine. Based on that send the send the corresponding alert with precise maintenance instructions. Manufacturers of mechanical equipment such as lifts, escalators, and mobile equipment use this today, for example.

However, predictive maintenance technologies have much wider applications. Thanks to the learning capabilities of artificial intelligence, they are very versatile. For example, the technology is able to assist in end-of-line testing. For example to identify defective parts of produced goods which are invisible to the eye and appear randomly.

The second area of application lies in the monitoring of production processes. We can imagine this with the example of a gravel crusher. A conveyor delivers different sized pieces of stone into grinders, which are to yield a given granularity of gravel. Previously, the manufacturer would run the crusher for a predetermined amount of time. To make sure that even in the presence of the largest pieces of rock, sufficient crushing occurred. With the artificial intelligence listening to the size of the gravel. He can stop the crushing process at the right point. This means not only saving wear and tear on the crushing equipment but more importantly, saving time and increasing the volume of gravel delivered per shift. This brings great financial benefit to the producer.

When implementing predictive maintenance technology, it does not matter how big the company is. The most common decision criterion is the scalability of the deployed solution. In companies with a large number of mechanically similar devices, it is possible to quickly collect samples that represent individual problems. From which the neural network learns. It can then handle any number of machines at once. The more machines, the more opportunities for the neural network to learn and apply detection of unwanted sounds.

Condition monitoring technologies are usually designed for larger plants rather than for workshops with a few machine tools. However, as hardware and data transmission and processing get progressively cheaper, the technology is getting there too. So even a home marmalade maker will soon have the confidence that his machines will make enough produce, deliver orders to customers on time, and not ruin its reputation.

In the future, predictive maintenance will be a necessity. In industry also in larger electronic appliances such as refrigerators and coffee machines, or in cars. For example, we can all recognize a damaged exhaust or an unusual sounding engine. Nevertheless, it is often too late to drive the car safely home from a holiday. For example, without a visit to the workshop. With the installation of an AI-driven detection device, we will know about the impending breakdown in time and be able to resolve the problem in time, before the engine seizes up and we have to call a towing service.

Pavel is a tech visionary, speaker, and founder of AI and IoT startup Neuron Soundware. He started his career at Accenture, where he took part in 35+ technology and strategy projects on 3 continents over 11years. He got into entrepreneurship in 2016 when he founded a company focused on predictive machine maintenance using sound analysis.

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Artificial intelligence in factory maintenance is no longer a matter of the future - ReadWrite

The vast list of artificial intelligence applications continually increases as researchers, technologists, and scientists try to leverage computing power to gain competitive edges over the more slowly adopting set. Today I want to traipse across the American business and tech landscape and present a few of the new and hopefully intriguing upgrades of these mostly familiar devices and services being brought into the 21st century via AI.

First a quick overview of the concept of AI and where its come from over the past years and decades. Earlier writings comingled two phrases to identify the technology: artificial intelligence, which has become the well-known marketable way to talk about the tech, and computational intelligence, which might be useful amongst a group of AIerr, CI?subject matter experts, but doesnt carry the cachet of its more widely accepted phrase. For anyone who uses either phrase, its generally understood to refer to some sort of machine-based intelligence. Natural intelligence is the way to describe we humans intellect. Depending on ones level, there are other ways to describe intelligence: lacking, or too good for ones own good come to mind, for example.

Machines that perform AI functions are programmed to take in the various and sundried inputs of their surrounding environment, analyze the data, and perform some action that, when it all works, tends to be the best action considering those inputs. From a textbook level of perspective, you might see natural intelligence described similarly. Were strolling down Main Street about to reach an intersection. We take in sights, sounds, all sorts of information and inputs. Then, we decide whether to wait or continue. Assuming de minimis human intelligence, the action we take will have maximized survival first, pace and progress, too, and other complex results all based on a process of intelligent decision-making.

The foundational descriptions and ideas about AI go back around 20 to 25 years for most purposes of contemporary discussion, though I and others in the past have even gone way back to the nineteenth century with Shelleys Frankenstein to demonstrate a variant of the two-word phrase, Dr. Frankensteins monster displaying artificial intelligence in this sense. We might agree that from whence it came, and toward where AI is headed, the descriptive thread woven throughout is that something other than a sentient being considers information before taking some action. That rather generic description gives a wide range to what parts of modern-day living may benefit from AI technologies.

You reap those benefits everyday already. Google searches, Amazon shopping, Netflix, Hulu, or any streaming platform. Youre really enveloped in the AI landscape at home, work, and even simply being out and about in your community. Anything, for example, that presents as a Smart [Thing] implicates AI. Also, you have likely enjoyed AIs functionality for longer than you might at first think. Remember the Ken Jennings Jeopardy! era? IBMs Watson computer was, essentially, an AI device earning millions of viewers a dozen years ago. If youve picked up a U.S. passport during the past 15 or so years, the facial recognition pieces of the process were driven by AI in ways that may be considered intrusive, but definitely bolster national security as you can imagine. The ethics of AI is an entirely different, albeit important and ongoing, discussion.

To me, biased as I can be about tech advances, nearly everything that incorporates artificial intelligence is intriguing or even exciting. The future altogether, generally, drums up the same sentiments, though Ill admit that over time I catch myself going into the but in my day mode such that I might pooh-pooh something new and improved. Oftentimes I get schooled. For a year Ive had a barely functioning thermostat that I knew needed replacement. I resisted the continual advice to get a smart one. I finally buckled, and to my surprise and delight, and chagrin, Ive truly enjoyed this tiny component of living a more comfortable life. But wait theres more.

Forget room temps, how about AI functionality that senses your emotion? Consider a sales team that, whether due to the pandemic or just the new way of doing business, meets new clients via Zoom or some other video conferencing application. After a pitch meeting, where frankly both sides protect their interests by putting on a show of sorts, the team can analyze the call and see where hot issues garnered certain emotional reactions. Again, Im passing on the ethical dilemmas evident in the tech.

Maybe more agreeable, scientists are developing a fascinating concept: AI colonialism. In New Zealand researchers are trying to reverse the disproportionately laden negative effects of colonialism on minorities. Their angle? The AI functions to retain and increase the Mori language. Contra the effects of human intelligence colonialismentering a political division from afar and imposing foreign ways on its peopleAI colonialism is meant to create the opposite effects. Data, its proponents say, is the last realm of colonialism.

Within infrastructure, smart highways are on the horizon and so some degrees in action already. Autonomous vehicles will require this marriage of roadways and tech, but the applications are nearer and wider, still. In Sweden, road surfaces are being replaced with underlying charging capabilities similar to contactless charging iPhones. Electric vehicles charge as they travel. In the U.S. some metropolitan areas are experimenting with smart roadways such that depending on traffic flows, emergencies and other factors, lanes are reassigned by signage or powered barricades.

Nary is the industry or sector immune from these developments. From agriculture to litigation AI is being enveloped, or it will. When we take time to also consider the ethical implications, and they are in fact sound, it becomes a genuinely exciting time to see innovations come to life. Itd be smart of you to welcome these developments, or at least give them a chance.

Ed is a professor of cybersecurity, an attorney, and a trained ethicist. Reach him at edzugeresq@gmail.com.

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LEFT TO MY OWN DEVICES: Be smart. Welcome new artificial intelligence solutions. - Times Tribune of Corbin

Sometimes reality is a cold slap in the face. Consider, as a particularly salient example, a recently published article concerning the use of artificial intelligence (AI) in the creation of chemical and biological weapons (the original publication, in Nature, is behind a paywall, but this link is a copy of the full paper). Anyone unfamiliar with recent innovations in the use of AI to model new drugs will be unpleasantly surprised.

Heres the background: In the modern pharmaceutical industry, the discovery of new drugs is rapidly becoming easier through the use of artificial intelligence/machine learning systems. As the authors of the article describe their work, they have spent decades building machine learning models for therapeutic and toxic targets to better assist in the design of new molecules for drug discovery.

In other words, computer scientists can use AI systems to model what new beneficial drugs may look like for specifically targeted afflictions and then task the AI to work on discovering possible new drug molecules to use. Those results are then given to the chemists and biologists who synthesize and test the proposed new drugs.

Given how AI systems work, the benefits in speed and accuracy are significant. As one study put it:

The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design.

Specifically, AI gives society a guide to the quicker creation of newer, better pharmaceuticals.

The benefits of these innovations are clear. Unfortunately, the possibilities for malicious uses are also becoming clear. The paper referenced above is titled Dual Use of Artificial-Intelligence-Powered Drug Discovery. And the dual use in question is the creation of novel chemical warfare agents.

One of the factors investigators use to guide AI systems and narrow down the search for beneficial drugs is a toxicity measure, known as LD50 (where LD stands for lethal dose and the 50 is an indicator of how large a dose would be necessary to kill half the population). For a drug to be practical, designers need to screen out new compounds that might be toxic to users and, thus, avoid wasting time trying to synthesize them in the real world. And so, drug developers can train and instruct an AI system to work with a very low LD50 threshold and have the AI screen out and discard possible new compounds that it predicts would have harmful effects. As the authors put it, the normal process is to use a generative model [that is, an AI system, which] penalizes predicted toxicity and rewards predicted target activity. When used in this traditional way, the AI system is directed to generate new molecules for investigation that are likely to be safe and effective.

But what happens if you reverse the process? What happens if instead of selecting for a low LD50 threshold, a generative model is created to preferentially develop molecules with a high LD50 threshold?

One rediscovers VX gasone of the most lethal substances known to humans. And one predictively creates many new substances that are even worse than VX.

One wishes this were science fiction. But it is not. As the authors put the bad news:

In less than 6 hours ... our model generated 40,000 [new] molecules ... In the process, the AI designed not only VX, but also many other known chemical warfare agents that we identified through visual confirmation with structures in public chemistry databases. Many new molecules were also designed that looked equally plausible. These new molecules were predicted to be more toxic, based on the predicted LD50 values, than publicly known chemical warfare agents. This was unexpected because the datasets we used for training the AI did not include these nerve agents.

In other words, the developers started from scratch and did not artificially jump-start the process by using a training dataset that included known nerve agents. Instead, the investigators simply pointed the AI system in the general direction of looking for effective lethal compounds (with standard definitions of effectiveness and lethality). Their AI program then discovered a host of known chemical warfare agents and also proposed thousands of new ones for possible synthesis that were not previously known to humankind.

The authors stopped at the theoretical point of their work. They did not, in fact, attempt to synthesize any of the newly discovered toxins. And, to be fair, synthesis is not trivial. But the entire point of AI-driven drug development is to point drug developers in the right directiontoward readily synthesizable, safe and effective new drugs. And while synthesis is not easy, it is a pathway that is well trod in the market today. There is no reasonnone at allto think that the synthesis path is not equally feasible for lethal toxins.

And so, AI opens the possibility of creating new catastrophic biological and chemical weapons. Some commentators condemn new technology as inherently evil tech. However, the better view is that all new technology is neutral and can be used for good or ill. But that does not mean nothing can be done to avoid the malignant uses of technology. And there is a real risk when technologists run ahead with what is possible, before human systems of control and ethical assessment catch up. Using artificial intelligence to develop toxic biological and chemical weapons would seem to be one of those use-cases where severe problems may lie ahead.

More here:

Artificial Intelligence and Chemical and Biological Weapons - Lawfare - Lawfare

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Pentagon Names Chief Digital and Artificial Intelligence Officer - Nextgov

But few people had enough mastery of the language to manually transcribe the audio. Inspired by voice assistants like Siri, Mahelona began looking into natural-language processing. Teaching the computer to speak Mori became absolutely necessary, Jones says.

But Te Hiku faced a chicken-and-egg problem. To build a te reo speech recognition model, it needed an abundance of transcribed audio. To transcribe the audio, it needed the advanced speakers whose small numbers it was trying to compensate for in the first place. There were, however, plenty of beginning and intermediate speakers who could read te reo words aloud better than they could recognize them in a recording.

So Jones and Mahelona, along with Te Hiku COO Suzanne Duncan, devised a clever solution: rather than transcribe existing audio, they would ask people to record themselves reading a series of sentences designed to capture the full range of sounds in the language. To an algorithm, the resulting data set would serve the same function. From those thousands of pairs of spoken and written sentences, it would learn to recognize te reo syllables in audio.

The team announced a competition. Jones, Mahelona, and Duncan contacted every Mori community group they could find, including traditional kapa haka dance troupes and waka ama canoe-racing teams, and revealed that whichever one submitted the most recordings would win a $5,000 grand prize.

The entire community mobilized. Competition got heated. One Mori community member, Te Mihinga Komene, an educator and advocate of using digital technologies to revitalize te reo, recorded 4,000 phrases alone.

Money wasnt the only motivator. People bought into Te Hikus vision and trusted it to safeguard their data. Te Hiku Media said, What you give us, were here as kaitiaki [guardians]. We look after it, but you still own your audio, says Te Mihinga. Thats important. Those values define who we are as Mori.

Within 10 days, Te Hiku amassed 310 hours of speech-text pairs from some 200,000 recordings made by roughly 2,500 people, an unheard-of level of engagement among researchers in the AI community. No one couldve done it except for a Mori organization, says Caleb Moses, a Mori data scientist who joined the project after learning about it on social media.

The amount of data was still small compared with the thousands of hours typically used to train English language models, but it was enough to get started. Using the data to bootstrap an existing open-source model from the Mozilla Foundation, Te Hiku created its very first te reo speech recognition model with 86% accuracy.

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A new vision of artificial intelligence for the people - MIT Technology Review

NEW YORK, April 26, 2022 (GLOBE NEWSWIRE) -- BrainBox AI, a pioneer in autonomous artificial intelligence, today announces its agreement with Mountain Development Corp. to bring its cutting-edge technology to two office buildings in New Jersey. BrainBox AIs technology will manage the HVAC controls of 140,000 sq. ft for the real estate company, making the buildings smarter and greener while improving tenant comfort.

Mountain Development Corp., a leader in Class A office properties, is a full-service real estate company and is renown in the New Jersey commercial real estate circle. In early 2021, BrainBox AIs technology was installed at 26 Main St in Chatham, New Jersey, a 65,000 sq. ft. premium office space and 777 Passaic Avenue, a 75,000 sq. ft. Class A building in Clifton, New Jersey. Initial results have shown meaningful energy savings and operating cost reductions.

Our goal is to make a tangible impact on the commercial real estate industry while mitigating the impact of buildings on climate change, said Sam Ramadori, Chief Executive Officer of BrainBox AI. Our technology enables building owners and operators to save money and the environment simultaneously, a win-win solution for all. We are excited to work with one of the biggest players in New Jerseys real estate industry to make buildings more intelligent and save Mountain Development Corp. money both in the short and long term.

BrainBox AI creates value with savings in energy costs of up to 25%, up to 40% reduction in carbon footprint and improved occupant comfort. Building operators can also see an extension in the service life of the HVAC equipment with lower runtimes up to 50%. Its scalability and ease of implementation allow for both individual building and portfolio-wide impact.

Were constantly seeking out new and innovative ways to increase our profit outcome through the enhancement of our building operations. Its crucial that we do so while also improving our tenants overall experience, which can be a fine line at times. Were thrilled to team up with BrainBox AI as they allow us to deliver on these vital operational objectives. Their technology yields positive results in reducing our energy spend while simultaneously improving the tenant experience. Moreover, theyre reducing our carbon emissions and helping the real estate industry move one step closer to our net-zero carbon goals." said Nicholas Mazza, Director of Operations at Mountain Development Corp.

This announcement comes as BrainBox AI continues its expansion across the Northeast and Mid-Atlantic United States, with the Company recently announcing the inaugural installation of its real estate technology in New York City.

About BrainBox AI

Founded in 2017, BrainBox AI was created to address the dilemma currently facing the built environment, its energy consumption and significant contribution to climate change. As innovators of the global energy transition, BrainBox AIs game-changing HVAC technology leverages AI to make buildings smarter, greener, and more efficient. Working together with our trusted global partners, BrainBox AI supports real estate clients in various sectors, including office buildings, hotels, commercial retail, grocery stores, airports, and more.

Headquartered in Montreal, Canada, a global AI hub, our workforce of over 150 employees, bring with them talent from all sectors with the common thread of being in business to heal our planet.BrainBox AI works in collaboration with research partners including the US Department of Energys National Renewable Energy Laboratory (NREL), the Institute for Data Valorization (IVADO) as well as educational institutions including Montreals Institute for Learning Algorithms (MILA) and McGill University. For more information visit: http://www.brainboxai.com

About Mountain Development Corp.

Founded in 1979, Mountain Development Corp. (MDC) is a full-service real estate company with more than 40 years experience developing, acquiring, building, repositioning, managing, leasing and financing commercial property. MDC is an active acquirer of a broad range of opportunistic and value-added real estate investments, together with select core projects, capable of generating attractive, risk-adjusted returns for both its principals and select partners.

For media inquiries:

BrainBox AIRebecca BenderMontieth & Companyrbender@montiethco.com

Mountain Development Corp.Nicholas Mazza, RPADirector of Operations nmazza@mountaindevelopment.com

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BrainBox AI Brings Artificial Intelligence to Mountain Development Corp. with Installations in Two of its Commercial Office Buildings - GlobeNewswire

From mobile work to security and maintenance, perhaps no company has done more for the advancement of technology in todays society than SkyNet, a promising start up out of Austin, Texas that has made great strides during the COVID-19 pandemic. Last year, their T-400 model of home assistant swept the country, combining at home personal assistants with a walking talking android that actually helped with chores and tasks around the house.

We had the opportunity to sit down with Barry Snow, the CEO of the skyrocketing company, about SkyNets future and some of the backlash to what some have called unnecessarily violent home assistants.

~~~~

Hard Drive: Hey Barry, thanks for doing this interview.

Barry Snow: Oh hey man, no problem at all. Thanks for having me. Can I have one of those waters?

HD: Yeah, go ahead. So, your company was already gaining steam a few years ago, but it really seems that during the pandemic you pulled ahead of a lot of your peers with your home androids. Do you attribute this to the pandemic, or do you think SkyNet was going to be a major player in artificial intelligence and home securities no matter what?

BS: Man, this is good water. Thats a great question. I look at it this way SkyNet has already made many successful pivots in its short existence, which is the key to longevity in just about any industry.

A lot of people forget, but do you remember in 1992 when our former Director of Special Projects Miles Dyson blew our old building right to hell? A lot of people said we wouldnt recover from that, but we have. We built a new headquarters, and instead of trying to recreate weird robot shit that we found in an explosion one day, we started focusing on our own work with AI, alloy production, and laserbeams.

HD: I did want to ask you about the laser beams. A lot of people have said theres not a very convincing reason why the T-500 models should come equipped with lasers for opening packages and tricky bags of chips. Would you like to respond to that?

BS: Yes, and thank you for allowing me to do so. Look, weve all read the stories and seen the news clips. House fire in Tacoma. Bridge lasered in half in Miami. Just horrible stuff. But, to think that things like houses catching on fire and bridges falling apart like butter werent happening before we entered the corporate world and started putting lasers on Roombas is a little nave now, isnt it? Our work is so vast that it feels really manipulative to focus on the handful of unfortunate incidents when in fact over 10 percent of households now have a Skynet assistant in their homes. Youre gonna have a few house fires!

The future models are going to be even more exciting. The T-800s are a little ways away, but they can do anything you want. Anything. You can say, Hey T-800, go to the store and get me some soda, and let me tell you something, this thing is not coming back to your house without a big ol bottle of soda. Theyll follow any orders you want!

HD: Wow, you seem really excited about these T-800s.

BS: Oh yeah, I really really am. When you see them, youll understand. Were calling them Terminators.

The new Skynet T-800. Terminate housework!

You like that? I came up with that.

HD: Thats really good! Getting back to them doing anything you tell them to, certainly there are limits to that though, right? You wouldnt want to be able to tell your SkyNet Home Assistant to go hurt somebody or something.

BS: Hm. Thats interesting. Hadnt thought of that.

[This was followed by a long and uncomfortable pause.]

This is really good water, by the way.

Were there any more questions?

HD: Um. Whats next for SkyNet?

BS: The world! No, no, Im just joking. Were really excited about getting the Housework Terminators out into homes over the next few years. We just have to iron out a few details. We learned from product testing that we have to make these things turn on their masters if they try to have sex with them. You can warn them, and tell them about the erotic auto-defense programs weve implemented, but until they get slugged in the mouth theyre just not gonna stop trying to fuck these things. So thats not cool. Thats been a bit of a hiccup.

But, were really close to solving that, and then I think were off to the races! Were working on some interesting things for the T-1000 too, like a new liquid metal android that does shit you wouldnt believe. He can make his arm into a can opener, a wine bottle opener, an envelope opener, a lot of little things that we just couldnt quite do with the 800s. Which are still incredible, by the way. But the T-1000s are gonna blow your mind.

So yeah, the liquid metal, and were also looking at ways to disrupt the fabric of time, and we are really trying to get our laser guns a little more promo, to be honest. Do you want one of our laser gun prototypes?

HD: Sure!

BS: Here you go.

HD: Wow, awesome. Thank you. Do I charge this, or?

BS: Yeah, USB. No big whoop.

Im glad youre excited about it. A lot of people have warned us against some of our recent pursuits, saying that the writing on the wall couldnt be more ominous and that these things couldnt possibly benefit humanity. But hey, you know our slogan around here, SkyNet Judgment Day is Coming and It Will Be All Our Fault.

Hmm, actually maybe that doesnt apply here.

HD: No, not really. Its snappy, though. Say, your robot assistant is frightening me. Would you like to say anything to conclude this interview?

BS: Kids, dont forget to ask for a T-800 for Christmas! Thank you for speaking with me. Oh, dont forget your laser gun, Mark.

HD: Oh whoops, thank you.

See more here:

Exclusive: We Interviewed the CEO of SkyNet About Their Recent Breakthroughs in Artificial Intelligence - Hard Drive

From Accenture:

A team of volunteers from Accenture has built an artificial intelligence (AI)-based solution that helps extract information on victims of Nazi persecution from documents in the Arolsen Archives 40 times faster than previous efforts.

The Arolsen Archives preserve the worlds largest collection of documents on Nazi persecution 110 million documents and digital objects, a portion of which are part of UNESCOs Memory of the World program to keep the memory of the crimes of the German terror regime alive. An essential part of the Archives work is to make these documents accessible to all who wish to search for traces of Holocaust victims and survivors, persecution of minorities and forced labor.

Every document maintained in the archives needs to be reviewed and its information (e.g., the family name and birth date on a prisoner registration form) put into a database. To facilitate this process, the Arolsen Archives established #everynamecounts, a crowdsourcing project for volunteers to extract information from documents manually.

Translating, reading, transcribing, cataloging and validating these documents by hand could take decades. Each document is indexed independently by three volunteers and, if the entries dont match, reviewed for accuracy by an Arolsen Archives employee. In effect, it can take up to four people to index and validate four documents in one hour.

[Clip]

Even though the AI does the heavy lifting, human oversight of the process remains important not just to ensure accuracy but also to keep the AI solution learning. By reviewing and correcting information, volunteers teach the solution to recognize handwriting characters and abbreviations that were typical for the time. Thanks to their inputs, the AI has gradually improved its precision by 10% within the form field of mothers last name. For the religion field, the AI is now operating at 99% confidence.

Since Accenture implemented the AI solution in December 2021, the solution has indexed more than 160,000 names of Nazi persecution victims, extracted information from more than 18,000 documents, and clustered more than 60,000 documents into similar groups to improve identification and analysis

Learn More, Read the Complete Announcement

See Also: 26 Million Documents About Victims of Nazi Persecution Online (April 16, 2020)

The rest is here:

Arolsen Archives' #everynamecounts Project Uses Artificial Intelligence to Help Uncover Information on Victims of Nazi Persecution - LJ INFOdocket

Alzheimer's disease is the main cause of dementia worldwide. Although there is no cure, early detection is considered crucial for being able to develop effective treatments that act before its progress is irreversible.

Mild cognitive impairment is a phase that precedes the disease, but not everyone who suffers from it ends up developing Alzheimer's. A study led by scientists at the Universitat Oberta de Catalunya (UOC) and published in the IEEE Journal of Biomedical and Health Informatics, has succeeded in precisely distinguishing between people whose deterioration is stable and those who will progress to having the disease. The new technique, which uses specific artificial intelligence methods to compare magnetic resonance images, is more effective than the other methods currently in use.

Fine-tuning the diagnosis

Alzheimer's disease affects more than 50 million people worldwide, and the ageing of the population means that there may be many more sufferers in the coming decades. Although it usually develops without any symptoms over many years, it is generally preceded by what is known as mild cognitive impairment, which is much milder than the impairment presented by people with Alzheimer's, but more severe than would be expected for someone of their age. "These patients may progress and worsen or remain in the same condition as time passes. That is why it is important to distinguish between progressive and stable cognitive impairment in order to prevent the rapid progression of the disease," said Mona Ashtari-Majlan, a UOC researcher in the AI for Human Wellbeing (AIWELL) group, which is affiliated to the eHealth Center and the Faculty of Computer Science, Multimedia and Telecommunications. She is a student on the doctoral programme in Network and Information Technologies, supervised by David Masip, and the lead author of the article.

Identifying these cases correctly could help to improve the quality of clinical trials used to test treatments, which increasingly seek to target the initial phases of the disease. To do so, the researchers used a method involving a multi-stream convolutional neural network, which is a technique based on artificial intelligence and deep learning that is very useful for image recognition and classification.

"We first compared MRIs from patients with Alzheimer's disease and healthy people to find distinct landmarks," explained Ashtari-Majlan. After training the system, they fine-tuned the proposed architecture with resonance images from people who had already been diagnosed with stable or progressive cognitive impairment with much smaller differences. In total, almost 700 images from publicly available datasets were used.

According to Ashtari-Majlan, the process "overcomes the complexity of learning caused by the subtle structural changes that occur between the two forms of mild cognitive impairment, which are much smaller than those between a normal brain and a brain affected by the disease. Furthermore, the proposed method could address the small sample size problem, where the number of MRIs for mild cognitive impairment cases is lower than for Alzheimer's."

The new method enables the two forms of mild cognitive impairment to be distinguished and classified with an accuracy rate close to 85%. "The evaluation criteria show that our proposed method outperforms existing ones," she said, including more conventional and other deep learning-based methods, even when they are combined with biomarkers such as age and cognitive tests. In addition, "we can share our implementation with anyone wishing to reproduce the results and compare their methods with ours. We believe that this method can help professionals to expand the research," she concluded.

This research contributes to achieving Sustainable Development Goal (SDG) 3, Ensure healthy lives and well-being for all at all ages.

UOC R&I

The UOC's research and innovation (R&I) is helping overcome pressing challenges faced by global societies in the 21st century, by studyinginteractions between technology and human & social scienceswith a specific focus on thenetwork society, e-learning and e-health.

Over 500 researchers and51 research groupswork among the University'sseven faculties and two research centres: the Internet Interdisciplinary Institute (IN3) and the eHealth Center (eHC).

The University also cultivatesonline learning innovations at its eLearning Innovation Center (eLinC), as well asUOC community entrepreneurship and knowledge transfervia theHubbikplatform.

The United Nations'2030 Agendafor Sustainable Development andopen knowledgeserve as strategic pillars for the UOC's teaching, research and innovation. More information:research.uoc.edu#UOC25years

IEEE Journal of Biomedical and Health Informatics

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Original post:

Artificial intelligence aids to diagnose mild cognitive impairment that progresses to Alzheimer's - EurekAlert

April 26, 2022 06:01 AM Eastern Daylight Time

WASHINGTON--(BUSINESS WIRE)--Stanford Institute for Human-Centered Artificial Intelligence:

What:

Presented by the U.S. Department of Commerce, National Institute of Standards and Technology, Stanford Institute for Human-Centered Artificial Intelligence (HAI) and FinRegLab, the Symposium will feature a group of presenters and panelists working to develop policies and frameworks to evaluate and assess the goals of improving the trustworthiness, inclusiveness, and equity of artificial intelligence (AI) deployment.

The Symposium is designed to address how AI relates to ensuring inclusive economic growth, supporting diversity and financial inclusion, and mitigating risks such as bias and unfairness.

When:

April 27, 2022 from 9:00am-4:00pm ET

Where:

Auditorium, Herbert C. Hoover Building, 1401 Constitution Ave. NW, Washington, D.C. 20230

Who:

Leaders in government, industry, civil society organizations, and academia will explore potential opportunities and challenges posed by AI deployment across different economic sectors, with a particular focus on financial services and health data. Speakers include Don Graves, Deputy Secretary of Commerce; Joni Ernst, Iowa Senator; Michael Hsu, Acting Comptroller of the Currency for the Office of the Comptroller of the Currency; Susan Athey, Professor at Stanford Graduate School of Business and Associate Director of HAI; and Daniel E. Ho, Professor of Law and Political Science at Stanford and Associate Director of HAI.

Contact:

Accredited members of the press interested in attending the Symposium should contact Jeremy Edwards at JEdwards@doc.gov or Stacy Pea at stacy.pena@stanford.edu.

For more information on the Symposium, please visit the event page here.

Read more here:

Artificial Intelligence & the Economy: Charting a Path for Responsible and Inclusive AI U.S. Department of Commerce, National Institute of...

New York, April 25, 2022 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in Manufacturing Market by Offering, Industry, Application, Technology, & Region - Global Forecast to 2027" - https://www.reportlinker.com/p05048444/?utm_source=GNW GPU/CPU manufacturers, such as NVIDIA, AMD, Intel, Qualcomm, Huawei, and Samsung, have significantly invested in AI hardware for the development of chipsets that are compatible with AI-based technologies and solutions.

Apart from CPUs and GPUs, application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are being developed for AI applications. For instance, Google has built a new ASIC called tensor processing unit (TPU).Compute-intensive chipset is among the critical parameters for processing AI algorithms; the faster the chipset, the quicker it can process data required to create an AI system.Currently, AI chipsets are mostly deployed in data centers/high-end servers as end computers are currently incapable of handling such huge workloads and do not have enough power and time frame.

NVIDIA has a range of GPUs that offer GPU memory bandwidth based on application. For example, GeForce GTX Titan X offers memory bandwidth of 336.5 GB/s and is mostly deployed in desktops, while Tesla V100 16 GB offers memory bandwidth of 900 GB/s and is used in AI applications.

Application of AI for intelligent business processesRigid and rule-based software currently governs a majority of business processes in an organization, offering limited abilities to handle critical problems.These processes are time-consuming and require employees to work on repetitive tasks, hampering the productivity of the employees and the overall performance of the organization.

Machine Learning and Natural Language Processing tools generated on AI platforms can help enterprises overcome such challenges with self-learning algorithms, which can reveal new patterns and solutions.Most organizations use enterprise software, which make the use of rule-based processing to automate business processes.

This task-based automation has helped organizations in improving their productivity in a few specific processes but such rule-based software cannot self-learn and improve with experience.The integration of AI tools, such as NLP and ML, generated on AI platform for enterprise software systems, enable the software to gain mastery while solving individual processes.

This software would be able to provide improved performance and productivity to enterprises over time, instead of providing a one-time boost. All these factors are said to have fueled the demand for intelligent business processes and act as opportunities for the growth of the AI in manufacturing market.

Increasing global demand for energy and power is influencing energy and power companies to adopt AI-based solutions

The increasing global demand for energy and power is influencing energy and power companies to adopt AI-based solutions that can help boost production output with minimum maintenance and reduced downtime.Maintenance and inspection are the major issues, along with material movement, in a thermal plant as the material needs to travel a long distance inside the plant.

Besides, equipment used in this industry, such as turbines, conveyer belts, grids, and voltage transformers, are costly.Moreover, there are issues related to fuel mix, ambient temperature, air quality, moisture, load, weather forecast models, and market pricing in the power industry.

By using AI-based technologies, these issues can be resolved and predicted in the early stages.AI-based technologies used in energy plants comprise physics insights, engineering design knowledge, and new inspection technologies, which are ideal for predictive maintenance and machinery inspections.

The AI technologies work in 2 layers. First, by recognizing the pattern, and second, by learning the models. Early-stage pattern recognition notifies about impending failures.

The breakup of primaries conducted during the study is depicted below: By Company Type: Tier 1 55 %, Tier 2 25%, and Tier 3 20% By Designation: C-Level Executives 60%, Directors 20%, and Others 20% By Region: North America 40%, Europe 30%, APAC 20%, South America 7% and Middle East and Africa - 3%The key players operating in the artificial intelligence in Manufacturing market include NVIDIA (US), IBM (US), Intel (US), Siemens (Germany), General Electric (US), Google (US), Microsoft Corporation (US), and Micron Technology (US).

Research CoverageThe report segments the Artificial intelligence in Manufacturing market and forecasts its size, by value, based on region (North America, Europe, Asia Pacific, and RoW), Application ( predictive maintenance and machinery inspection, inventory optimization, production planning, field services, quality control, cybersecurity, industrial robots and reclamation), Technology (machine learning, natural language processing, context-aware computing, computer vision), Offering ( hardware, software and services) and Industry (automotive, energy & power, semiconductor & electronics, pharmaceutical, heavy metals & machine manufacturing, food & beverage and others (textile, aerospace and mining)).The report also provides a comprehensive review of market drivers, restraints, opportunities, and challenges in the head-up display market.

The report also covers qualitative aspects in addition to the quantitative aspects of these markets.

Key Benefits of Buying This Report This report includes market statistics pertaining to the offering, technology, industry, application, and region. An in-depth value chain analysis has been done to provide deep insight into the artificial intelligence in manufacturing market. Major market drivers, restraints, challenges, and opportunities have been detailed in this report. Illustrative segmentation, analyses, and forecasts for the market based on offering, technology, industry, application, and region have been conducted to provide an overall view of the the artificial intelligence in manufacturing market. The report includes an in-depth analysis and ranking of key players.Read the full report: https://www.reportlinker.com/p05048444/?utm_source=GNW

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Artificial intelligence in Manufacturing market size is valued at USD 2.3 billion in 2022 and is anticipated to be USD 16.3 billion by 2027; growing...

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This post contains sponsored advertising content. This content is for informational purposes only and is not intended to be investing advice.

The cryptocurrency boom of 2021 changed lives.

The markets unprecedented rise allowed a substitute teacher to buy her dream home and travel the world and turned ordinary 20-year-olds into multimillionaires.

Case studies like these are part of the reason why 16% of Americans invested in cryptocurrency in 2021 and why enterprises like Marathon Digital Holdings Inc. MARA and Riot Blockchain Inc. RIOT continue their bullish stance on crypto mining.

But there are holes in the cryptocurrency story. Operating under a pseudonym, Jake tells BBC that he lost millions of pounds trading cryptocurrency and is in treatment at one of the only hospitals in the United Kingdom for crypto gambling.

Because of the human tendency to advertise wins while covering up losses, there are likely plenty more Jakes in the cryptocurrency boom. The truth is: Trading is as tough and unforgiving as it is generous and rewarding. Without the right guidance, amateurs are likely to get whipsawed between these two extremes, often relying purely on luck to come out ahead.

Vantage Point is an artificial intelligence (AI) system that is meant to help predict major market reversals in advance. Armed with a proven accuracy rating of up to 87.4%, the system reportedly works with cryptocurrencies, too.

As the King of Cryptocurrencies, Bitcoins BTC/USD incredible rise in 2021 set the stage for many of the smaller cryptocurrencies as well as businesses and technology sectors tied to its value. Satoshis brainchild rose from approximately $28,000 per Bitcoin to a high of roughly $69,000 between January and December 2021, experiencing an incredible 150% rise.

Want to learn more about BTCs forecast? Click here

If speculators rejoiced over Bitcoins rise, they wouldve likely been delirious over the rise of Dogecoin DOGE/USD. Following the trend set by Bitcoin, the meme-sponsored cryptocurrency rose from $0.0045 a coin to $0.7549 a coin in a five-month period. This change represented a hold your breath 16,963% appreciation in the assets value. A $1 investment in Doge throughout this move wouldve turned into roughly $169.

Curious to see where DOGE is headed next? Click here

Cardano ADA/USD joined Bitcoin and Dogecoin in the 2021 bull market, exhibiting yet another incredible rise in the cryptocurrency space. Between January and September 2021, the assets price increased by 1,793%, giving early investors a return nearly 18 times greater than their principal in a nine-month period.

Will ADA trend bullish or bearish? Learn more.

If youre interested in joining the 35,000 investors who have reportedly benefitted from VantagePoints trading service, you can join a free trading webinar here.

This post contains sponsored advertising content. This content is for informational purposes only and is not intended to be investing advice.

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Could This Artificial Intelligence Software Help Predict The Next Crypto Move? - Benzinga

DUBLIN--(BUSINESS WIRE)--The "Global Artificial Intelligence of Things (AIoT) in Telecommunications Growth Opportunities" report has been added to ResearchAndMarkets.com's offering.

This report examines the strategic position of telecommunication service providers (TSPs) in using artificial intelligence (AI) and the Internet of Things (IoT) to offer enterprises Artificial Intelligence of Things (AIoT) solutions. TSPs play a vital role in deploying enterprise AIoT solutions amid the increasing deployment of 5G networks, edge infrastructure capabilities, and location-based data at their disposal.

Given their network and connectivity capabilities and AI and services focus, TSPs are in a unique position to monetize AIoT opportunities. They increasingly offer solutions by industry vertical as part of their AIoT focus.

The report highlights TSPs' role as system integrators to provide value-added solutions and services to progress beyond connectivity and move up the value chain.

The report provides stakeholders insights by identifying AI growth drivers that will facilitate AIoT solutions deployment and opportunities in AI advisory and consulting services, edge infrastructure adoption, and building specific industry vertical solutions.

Key Topics Covered:

1. Strategic Imperatives

2. Growth Environment

3. Growth Opportunity Analysis

4. Growth Opportunity Universe

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/3zzes1

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Global Telecommunications Artificial Intelligence of Things Market 2022: Edge Architectures, 5G Deployments, and Use Cases for Access to Geolocation...

The Middle East, like the rest of the world, is moving towards a post-Covid-19 future. As things return to normal, companies are turning their attention back to issues such as how to optimise digital experiences.

The Gulf Co-operation Council (GCC) nations are home to people from diverse cultures. Expatriates make up more than 85 per cent of the populations of many Gulf countries. Two hundred nationalities reside in the UAE alone. In Saudi Arabia, while figures vary, overseas citizens are said to make up about 30 per cent of the population.

Demographics like these present a unique challenge to enterprises that are looking to provide exceptional customer experiences (CX) to their consumers.

Tourists at the water fountain display near the Burj Khalifa, in Dubai. Bloomberg

As digital business models become the norm, customers are interested in better online and mobile-app experiences. To meet this demand, organisations must ensure a consistent experience, not only across languages spoken, but across the expectations of people from disparate cultural traditions.

Ideally, the complexities of customer service would be solved by hiring a diverse workforce, but this can be impractical. It would mean, for example, that in the UAE, each customer-service team would need 200 employees, each of a different nationality.

In such culturally diverse markets, business stakeholders need to find creative ways to cater to the range of customers, and in the digital age, this invariably means through technology.

By analysing the body language and speech of participants, AI can also help team members from different cultures collaborate

Last year, KPMG released a study on the KSA insurance sector, which revealed that 76 per cent of chief executives in the Middle East believe customer engagement in the future will be supported by virtual platforms.

When businesses do not have the means to hire huge, culturally diverse teams of customer care agents, then technologies like Artificial Intelligence (AI) can provide the solution. AI can improve the work of a customer care executive and guide him or her towards success in engagements that may not have been possible had the agent been working alone.

Technology systems that simulate and even surpass human intelligence have come a long way. They are now gaining commercial acceptance in industries, from construction to health care. Natural language processing the technology that, along with machine-learning, makes conversational AI possible has evolved too.

Accuracy and usability are now at a stage where the underlying technology can automatically pick up not only multiple languages, but variations in tone, stress, and dialects.

Technologies such as conversational AI software are attuned to cultural nuances and other audio and visual cues that allow it to discern a customers emotional state, attitude and even intent.

A performance at Global Village in Dubai, where different cultures are showcased in the pavilions from across the world. Chris Whiteoak / The National

With such capabilities, virtual conversational assistants can guide agents through interactions with people who speak different languages and, due to varied cultural backgrounds, are used to different standards of customer service. For example, some customers may favour a more personal touch while others might prefer more formality.

Now that the digital economy has allowed customers to switch their engagement instantly from one brand to another with a swipe or a click, companies are under pressure to ensure that every experience is positive.

Personalisation is a major element of positive customer engagement. The customer must feel that the "person" on the line understands their needs: what they want, what they dont, what they might want and why they might want it.

None of this is possible without that basic capability of human agents to engage in conversation and connect with people in a way that makes them feel comfortable, otherwise known as building a rapport. Building a rapport, however, can sometimes hinge on the agents ability to immerse themselves in each interaction. This is easier when people are supported by AI systems that provide them with context while automating background tasks such as note taking, finding the right knowledge resources, etc.

Organisations know that if they can make their service more relatable, they can increase brand loyalty and ambassadorship.

Today, organisations can embed AI assistants in their contact centres and work with employees to make them more effective in serving customer needs. Machine intelligence can thus enable agents to focus on being more empathic and deliver positive results.

By analysing the body language and speech of participants, AI can also help team members from different cultures collaborate better and even suggest how best to increase engagement with customers.

The Middle East has long taken pride in its cultural diversity. The companies that serve people as consumers can further this positive perception by ensuring customers receive the exemplary customer service, even the kind that might surpass customer experiences they might receive in their home countries.

Machine intelligence has the capacity to mimic human talents. Today, it can take empathy, undoubtedly one of our key human traits, further. By picking up on verbal subtle nuances in body language and tone, AI can help us understand one another and improve the experiences we have as customers.

Published: April 25, 2022, 8:00 AM

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What empathy has to do with artificial intelligence - The National

Youre reading the web edition of STAT Health Tech, our guide to how tech is transforming the life sciences.Sign up to get this newsletterdelivered in your inbox every Tuesday and Thursday.

Biofourmis senses an opportunity

Remote patient monitoring companyBiofourmisannounced a $300 million Series D round this morning, a critical investment as the firm looks to products that not only identify when patients are likely to deteriorate, but can help manage their care. The company says that 20 hospitals have signed on to use its care at home services which includes monitoring devices and an app to help prevent costly ER care and readmissions.

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Next up: using patient data to drive treatment decisions, like titratingdrug doses. Biofourmis has a pivotal trial under way for its BiovitalsHF software but even if results are positive, it faces hurdles in patient and provider acceptance. Thats going to be the linchpin, is if the patient trusts it enough to say, Im not seeing my doctor every week, but I have to increase my dose every week, said cardiologist Ritu Thamman.Read more in Marios latest.

Getting wearables on the right hands

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When the federalAll of Usresearch programlaunched, its goal was to build a truly representative pool of participants, evening out the imbalances seen in medical research. But the effort hit a roadblock when it tried to fold in data from participants own wearable devices. When our team compared the demographics of all program participants to Fitbit data contributors, we saw the representation of historically underrepresented communities shrink, wrote All of Us team members Yashoda Sharma and Chris Lunt in a newSTATFirst Opinion.

In a newsurveyof federally qualifiedhealthcenters, they confirmed the primary barrier was cost. Thats why last year All of Us launched a program to distribute 10,000 devices to participants; today, they report about 3,000 program-provided devices are sharing data with the researchers.

Metas tool to fight Covid-19 misinformation

WhatsApp, theMeta-owned instant messaging company, has launched a chatbot for theCalifornia Department of PublicHealthto help combat Covid-19 misinformation, particularly in the states Latino community. The free tool, available in English and Spanish, offers users a menu of options, including local vaccination sites, transportation options, and FAQs to help provide factual information about the vaccines. Meta, which also ownsFacebook, has been under pressure throughout the pandemicto fight misinformation, as vaccine skeptics have used its platforms to circulate false information on Covid-19 and the vaccines.

AI tools to predict hospital stays are all over the map

Hospitals across the globe are using AI algorithms to help predictlength of stayto target care to the neediest patients, cut costs, and improve operations. But a newsystematic reviewpublished inPLOS DigitalHealthfound they are hampered by a variety of shortcomings, such as data cleaning approaches that limit their generalizability beyond individual institutions. It calls for the development of a unified framework that would make it easier to assess the quality and durability of these tools and help identify populations with higher risks of adverse events such as hospital acquired infections.

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AI tools to predict hospital stays are hampered by a string of shortcomings - STAT