Vatican officials are calling for stricter ethical standards on the development of artificial intelligence, with tech giants IBM and Microsoft being the first companies to sign its new initiative.

The Rome Call for AI Ethics lays out six broad principles: transparency, inclusion, responsibility, impartiality, reliability, and security and privacy. These principles say that technology should protect people, particularly the weak and underprivileged. They also urge policymakers across the world to create new forms of regulation on advanced technologies that have a higher risk of impacting human rights, which includes facial recognition.

AI is incredibly promising technology that can help us make the world smarter, healthier, and more prosperous, IBM vice president John Kelly III said after the initiatives signing. But only if it is shaped at the outset by human interests and values.

The Vatican wants to ensure that companies are not using AI as a means to collect data without the consent of individuals and then using that data for commercial or political benefit. In one recent example, it was shown that thousands of federal government agencies and private companies were using software owned by face recognition company Clearview AI, which scraped facial data without peoples knowledge. The companys database, which features more than 3 billion images pulled from various online sites, is being used by law enforcement to catch persons of interest.

The document also says that a duty of explanation must be established and that AI-based algorithms should provide individuals with information on how these algorithms came to their decisions to ensure that there is no bias. Last year, US lawmakers introduced a bill that would do just that and allow the Federal Trade Commission to create rules that would force these companies to evaluate automated systems containing highly sensitive information.

Vatican officials hope to increase the number of signatories for its AI ethics initiative in the coming months. They also hope to collaborate with universities across the globe to promote more scientific research into ethical AI guidelines.

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The Catholic Church proposes AI regulations that protect people - The Verge

Imagine a world where A.I. is all around you. You get in your self-driving car to go to the doctors office, where a slew of tests are analyzed by machines that can diagnose your ailments with 99 percent accuracy. They give you a personalized prescription based on your individual biology, and then you go have a lunch of a cheeseburger and salad, one catering to your tastes and the other to your needs. Maybe you cheat and get fries with the cheeseburger, anyway.

For machines to accomplish that kind of work, they need the type of hardware that can handle the massive amount of data required. Thats where researchers from the Massachusetts Institute of Technology and Stanford University come in, as they recently developed a new type of three-dimensional chip made from different nanotechnologies that essentially puts the main two functions of chips under one roof. The chip streamlines the process and makes it easier for systems built from this chip to function as prescribed for A.I. systems.

Conventional chips basically come in two different flavors those for data storage, and those for processing, and they need to be linked in order to make the system run. In a paper published this month in the journal Nature, the research team outlines a new design for a chip that cobbles together both these functions.

The new chip is made of carbon nanotubes (sheets of 2D graphene morphed into nanocylinders) and resistive random-access memory (RRAM) cells, which charge the resistance of solid dielectric materials.

It might sound a bit complex, but what it basically means is that the RRAM and carbon nanotubes are stacked vertically over one another, creating a 3D architecture that lets a single chip fulfill multiple functions. This is beyond the capabilities of silicon-based chips.

Computers made with such a design could handle incredible amounts of bandwidth the type were likely going to need in complex computing structures that use A.I. and autonomous systems. Any machine learning applications would likely get a boost from a such a chip.

The technology could not only improve traditional computing, but it also opens up a whole new range of applications that we can target, said lead author Mark Shulaker in a statement. My students are now investigating how we can produce chips that do more than just computing.

The team is far away from demonstrating how the chip could be viably used in real world devices. But the fact that A.I. is still a work in progress gives the team plenty of time to figure out a sustainable way to manufacture and implement this chip in industrial and commercial applications.

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Scientists Create a 3D Chip Capable of Making AI Systems Work - Inverse

The coming days will be crucial for Turkey in determining a road map to development of artificial intelligence (AI). The Presidency's Digital Transformation Office, which is the commanding force behind Turkey's national AI strategy, recently brought together people from various sectors and fields as part of the last stage of a series of projects that have been ongoing for more than two years, to answer the following questions: What do you think our strategy should be? What should we look out for? How can you contribute?

Turkey's long-awaited strategy will be officially announced shortly after all these answers are evaluated. According to the Future of Life, 34 countries have already determined their national AI strategies. Turkey will soon be joining them, setting itself a clear course in the field of AI tech and developing moves accordingly.

Ali Taha Ko, president of the Digital Transformation Office, gave hints about what this strategy would encompass at the National Artificial Intelligence Strategy Workshop held at the Scientific and Technological Research Council of Turkey (TBTAK)'s Gebze Campus on Feb.19-22, which also saw the participation of Industry and Technology Minister Mustafa Varank.

Ko stated that they are set to become a country that not only consumes technology but also one that produces it.

Turkey's data will remain in Turkey. In this sense, we need to protect our AI models and the algorithms created with our national data as well as protecting it as a whole, he said.

Ko said being in the digital age, they are working not only with data but also with an understanding of governance aimed at generating value from such data.

One of the most important methods of generating value from data is AI technologies, Ko continued. AI is a data-driven system. Considered as the crude oil of our age, data is a resource used in both the training and testing of AI systems. This brings about the requirement of correct classification and labeling of data. Unfortunately, if your data is not labeled and classified correctly, it cannot be used in the correct manner.

Security of algorithms

In his speech, Ko also touched on the international debate on how healthy, and more precisely, how reliable, AI algorithms are, pointing to the criteria of quality and protection of data in this discussion.

As (a digital transformation) office, we say that Turkey's data will remain in Turkey. In this sense, we need to protect our data as well as the AI models and algorithms created with our national data because these algorithms can be prevented (from proceeding) or get stolen, and on top, our national data that creates this algorithm can be stolen with reverse engineering, which in itself constitutes a problem. So, one of the most important things we will do is to figure out how to secure our AI algorithms. We have to ensure the security of our AI algorithms in the same way.

Ethics cannot be overlooked

Ko pointed out that AI systems can be used as a decision support system especially in the field of health care.

When we start doing genome research with AI algorithms, we will be able to find a treatment for the novel coronavirus, and maybe even cancer, but perhaps all of these algorithms will also give birth to a biological weapon. Therefore, we need to discuss the ethical aspects of using AI in the field of health care and come to a final decision, he continued.

"For our AI algorithms to function properly, one of the most important things apart from ethics and law is the opportunity to work openly and efficiently. We need to have open data. Some even think that AI algorithms harbor the power to eliminate discrimination in society by contributing to social harmony. As well as the opportunity to access AI technologies equally... We think of it as a paradigm-shifting technology that will ensure global justice by providing equal access to all. Just as President Erdoan says, 'The world is bigger than five,' we say that the AI world is bigger than just two (China and the U.S., the most developed countries in the field for now). We think these are technologies that should be studied in terms of the welfare of whole humanity.

AI in the age of digital transformation

Ko pointed to the fact that AI is one of the most important stakeholders of the digital transformation field as it is in almost every other field.

With the increased use of AI, various concerns have emerged about how to ensure transparency, security and accountability, Ko said.

To eliminate the concerns that may arise, this system must first be human-centered, it must be fair, it must increase social welfare, it must be transparent, reliable, accountable, value-based, and dependent on national and ethical values. In our AI strategy, which should focus on human and ethical values, we have obligations such as creating a sustainable and production-based environment in Turkey by building an AI ecosystem, paving the way for work on AI in our country by completing the framework of data access, sharing and increasing the efficiency of all businesses and business processes in the public sector by expanding the use and application of AI technologies, sustaining this AI ecosystem by bringing up and educating qualified manpower, increasing the human benefit of each AI system to be produced, and ensuring its well-being."

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Experts to map out Turkey's strategy on AI centered on ethics and data protection - Daily Sabah

Forbes

This Bot's For You, How To Reprogram AI Bot Robots Forbes Artificial Intelligence (AI) has had something of a makeover and renaissance in recent times. We have stopped using the term to describe fantasy Sci-Fi robots and started to talk about real software robots that we often call 'bots' that we build to ...

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This Bot's For You, How To Reprogram AI Bot Robots - Forbes

About the author:Anish Sebastianco-founded Babyscripts in 2013, which has partnered with dozens of health systems for its data-centric model in prenatal care. As the CEO of the startup, Anish has focused his efforts on product and software development, as well as evidence-based validation of their product. Prior to this, he founded a research analytics startup and served as a senior tech consultant at Deloitte.

Last month saw the rollout of the latest upgrades to Amazons Echo speaker line: earbuds, glasses and a ring that connect to Amazons personal assistant Alexa. These new products are just three examples of a growing trend to incorporate technology seamlessly into our human experience, representing the ever-expanding frontiers for technology that have moved far past the smartphone.

These trends and others are going to make a big impact in the healthcare space, especially as providers, payers and consumers alike slowly but surely recognize the need to incorporate tech into their workflows to meet the growing consumer demand for digital health tools. At the same time, the data-hungry nature of these innovations is creating its own problems, driving a discussion around privacy and security that is louder and more urgent than ever.

Here are three trends to look out for in the coming year:

Its been quite a few years since AI has emerged from the pages of science fiction into our day-to-day reality, and the healthcare industry has provided a fertile proving ground for all aspects of its innovations. From software that analyzes medical data to identify patients for clinical trials in minutes, to software that analyzes medical images to diagnose tumors in milliseconds; from chatbots that perform administrative tasks like setting up an appointment to chatbots that empathize with human emotion and manage mental anxiety; AI in digital health has evolved by leaps and bounds.

In 2020, we will continue to see AI and ML push boundaries, while at the same time mature and settle into more defined patterns.

With the adoption of technologies like FaceID, facial recognition technology will be an important player in privacy and security intimate concerns of the healthcare field. It can be leveraged to drastically simplify the security requirements that make multi-factor authentication a time-consuming process for healthcare professionals on average, doctors spend 52hours a year just logging in to EHR systems. On the patient end, this same technology has the ability to detect emotional states of patients and anticipate needs based upon them, and the success of startups like Affectiva, the brainchild of MIT graduates, shows the tremendous promise of deep learning for these patient needs.

Then theres the tremendous capability of AI to accumulate massive amounts of data from monitoring systems, only matched by its ability to process and analyze this data. Were going to see AI play a major role in developing predictive algorithms to improve clinical interventions and mediate hospital readmissions.

Meanwhile, FDA-approved innovations from Microsoft and others claim the ability of computer vision for assisting radiologists and pathologists in identifying tumors and abnormalities in the heart. While robotic primary care is a long way off, some view AI as a rival to more niche clinical positions.

The progress and traction of AI and ML raise lots of questions: can algorithms predict risk of sepsis better than trained ICU clinicians? Can computer vision replace the work of the radiologist and pathologist? And even if that is to be the case, will consumers have difficulty buying into the power and promise of AI? The answers seem to rest in the industry working with stakeholders and policy-makers to develop the right frameworks for monitoring and regulating the use of AI.

2019 witnessed the fallout of the Cambridge Analytica scandal, and added several high profile data concerns of its own: Amazon workers paid to listen to Alexa recordings, for example, and the transfer of non-deidentified, personal health data of more than 50 million Americans to Google.

As the current generation fueled by smartphones, smart speakers, smart homes, smart everything wakes up to the serious challenges to privacy that these technological efficiencies are potentially introducing, theyre educating themselves about data sharing and becoming more cautious about the information that they are potentially sharing with third-party sites.

For companies that deal with special categories of sensitive data like medical information the stakes are much higher. Access to information such as mental health, sex life, family planning, history of disease, physical wellness, etc. could potentially jeopardize users job opportunities and promotions, and may even engender or perpetuate discrimination in the workplace.

In 2020, look for digital healthcare to establish increasingly tight security, clearly communicate privacy policies and provide more transparency around data use.

Interoperability is a major player in health tech innovation: patients will always receive care across multiple venues, and secure data exchange is key to providing continuity of care. Standardized APIs can provide the technological foundations for data sharing, extending the functionality of EHRs and other technologies that support connected care. Platforms like Validic Inform leverage APIs to share patient-generated data from personal health devices to providers, while giving them the ability to configure data streams to identify actionable data and automate triggers.

In the upcoming year, look for major players like Apple and Google to make strides toward interoperability and breaking down data silos. Apples Health app already is capable of populating with information from other apps on your phone. Add your calorie intake to a weight loss app? Time your miles with a running app? Monitor your bedtime habits with a sleep tracking app? Youll find that info aggregating in your Health app.

Apple is uniquely positioned to be the driver of interoperability, and Google is not far behind. They have a secure and established platform, trustworthy for the passage of encrypted data (such as patient portals), and command a brand loyalty ubiquitous in the US and elsewhere, not to mention pre-established relationships with the hospitals that are critical to making any true strides in that direction. Its a position that Apple has deliberately cultivated: as smartphone innovation falls into stalemate, theyre reaching toward bigger horizons in Tim Cooks words, improving health will be Apples greatest contribution to mankind.

These trends in digital health are not new. As with any innovations in healthcare, the process is slow and the cost of the payoff hotly debated, yet it is no longer a question of if, but when these innovations will start optimizing care, whether we like it or not.

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Opinion: AI, privacy and APIs will mould digital health in 2020 - MobiHealthNews

Artificial intelligence (AI) refers to the ability of machines to interpret data and act intelligently, meaning they can make decisions and carry out tasks based on the data at hand rather like a human does.

5 Reasons Why Artificial Intelligence Really Is Going To Change Our World

Chances are youve read a lot about AI in recent years particularly how its going to save the world and/or end civilization as we know it. Its certainly true that AI attracts a lot of hype and, shall we say, colorful predictions. But, unlike some technology trends, much of the hype surrounding AI is entirely justified. It truly is a transformative technology one that will dramatically alter our lives in very real ways.

Not quite convinced? Here are five reasons why I believe AI is going to change the world in 2020 and beyond.

1. AI is everywhere

Ever asked Alexa for the morning weather report, or passed through a public space that uses facial recognition technology, or paid for something using your credit card, or bought a product recommended to you by Amazon, or browsed potential love matches on a dating app? Of course, you have. Most of us have done one or all of these things, probably in the last week. Probably in the last 24 hours.

And, you guessed it, all of these everyday processes are underpinned by AI and data. AI allows your credit card company to determine in the blink of an eye that your latest transaction fits your spending pattern and isnt fraudulent. Mastercard, for example, uses AI algorithms to assess the 75 billion transactions a year processed on its network. So, to put it bluntly, AI is already deeply embedded in your everyday life, and its not going anywhere.

2. AI isnt just infiltrating everyday life, its going to transform entire industries

The impact of AI is already being felt in a wide range of industries, from banking and retail to farming and manufacturing. In healthcare, AI is being used to identify (and, in some cases, even predict) disease, helping healthcare providers and their patients make better treatment and lifestyle decisions.

AI systems can even outperform human experts when it comes to identifying disease; in January 2020, clinical trials of AI software developed by Google Health confirmed that the software was better at spotting signs of breast cancer in mammograms than radiologists. The system also flagged fewer false positive results than the experts.

3. AI will make us more human, not less

As machines become more intelligent, they can carry out more and more tasks leading to rising automation across most industries. With this rise in automation comes valid concerns about the impact on human jobs. But, while theres no doubt that automation will lead to the displacement of many jobs, I believe it will also create new jobs jobs that value our uniquely human capabilities like creativity and empathy.

AI will also make our working lives better. Journalism is one industry thats undergoing an AI revolution, and there are many AI tools that help journalists identify and write stories. At Forbes, for example, an AI-driven content management system called Bertie is used to identify real-time trending topics, suggest improvements to headlines, and identify relevant images. This reduces some of the behind-the-scenes legwork for human journalists, leaving them to focus on telling the story.

4. AI is becoming more affordable for the masses

It used to be that to work with AI youd need expensive technology and a huge team of in-house data scientists. Thats no longer the case. Like many technology solutions, AI is now readily available on an as-a-service basis with a rapidly growing range of off-the-peg service solutions aimed at businesses of all sizes.

As an example, in 2019, Amazon launched Personalize, an AI-based service that helps businesses provide tailored customer recommendations and search results. Incredibly, Amazon says no AI experience is needed to train and deploy the technology.

5. AI fuels other technology trends

Finally, as if we needed any more evidence that AI really is going to change the world, lets end with this simple fact: AI is the foundation on which many other technology trends are built.

Essentially, this means that, without AI, we wouldnt have achieved the amazing recent advances seen in areas like virtual reality, chatbots, facial recognition, autonomous vehicles, and robotics (and thats just to name a few). Think of almost any recent transformative technology or scientific breakthrough, and, somewhere along the way, AI has played a role. For example, thanks to AI, researchers can now read and sequence genes quickly, and this knowledge can be used to determine which drug therapies will be more effective for individual patients.

AI is just one of 25 technology trends that I believe will transform our society. Read more about these key trends in my new book, Tech Trends in Practice: The 25 Technologies That Are Driving The 4th Industrial Revolution.

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5 Reasons Why Artificial Intelligence Really Is Going To Change Our World - Forbes

Facebook today announced a new server design it calls Big Basin, a successor to its Big Sur line of artificial intelligence training systems. These Nvidia-powered GPU servers, tied together into large training networks for AI software, are what enable Facebook products to perform object and facial recognition and real-time text translation, as well as describe and understand the contents of photos and videos.

Big Basin can now train on learning models 30 percent larger than its predecessor, Facebook says. It can also crunch through the massive number sets used by an AI system to improve itself at nearly twice the speed, according to tests conducted on standardized neural network models.

Facebooks AI training systems just became faster and more capable

Facebook plans to make the server design open to the public in the near future. Thats standard at the company, which participates in and helped create the Open Compute Project for sharing and collaborating on data center hardware and software. So anyone even server design specialists in competing companies will soon be able to download the Big Basin schematics once theyre posted online.

For Facebook, its less about keeping under wraps the tools it uses to train AI systems and more about trying to advance what its AI systems are capable of. Its not just about pushing the limits of technology, though Facebook is among one of the largest organizations investing in cutting-edge and experimental AI research. The companys large investments in AI go hand-in-hand with its push toward live video and other consumer-centric focuses. If youve logged into Facebook, its very likely youve used some type of AI system weve been developing says Kevin Lee, a technical program manager at Facebook who works on Big Basin and other data center initiatives.

For instance, by tagging friends and categorizing videos including those streamed live Facebook may help drive more users to upload video and consume it. Theres also a large social impact the company can have with its AI research. One key function of Facebooks current AI algorithms today is describing the contents of photos to blind users, while just last week Facebook announced it would use AI-powered pattern recognition software to try and identify when troubled users may be in need of mental health outreach. All of this is made possible because the company invests in and continues to develop the servers, like Big Basin, that train these systems before theyre pushed out to public products.

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Facebook's new AI training server is nearly twice as fast - The Verge - The Verge

Aidoc, which bills itself as an AI solutions provider for radiologists, today announced that the U.S. Food and Drug Administration has allowed the use of its algorithms for adjunctive detection of findings associated with COVID-19. The models arent meant to replace traditional COVID-19 diagnostic tests, like serological tests and nasopharyngeal swabs, but the agencys allowance acknowledges they could be used to prioritize incidental (i.e., non-specific) CT findings tied with COVID-19 infections.

While the U.S. Centers for Disease Control and Prevention (CDC) recommends against the use of CT scans or X-rays for COVID-19 diagnosis, as does the American College of Radiology (ACR) and radiological organizations in Canada, New Zealand, and Australia, others assert systems from companies like Alibaba, RadLogics, Lunit, DarwinAI, Infervision, Qure.ai, and now Aidoc might play a role in triage by indicating further testing is required. Even the best AI systems sometimes cant tell the difference between COVID-19 and common lung infections like bacterial or viral pneumonia, but recent studies show that as many as 10% of asymptomatic patients undergoing CT scans for other conditions were discovered to have COVID-19.

Aidoc which has four FDA cleared solutions, meaning theyve been proven substantially equivalent to other similar legally marketed products says its COVID-19 tool helps to isolate anomalies in CT studies containing the lung or part of the lung. Concretely, that includes the chest, abdomen, and cervical spine.

In our experience, it is not unusual for the radiologist to be the first to diagnose COVID-19 disease in patients, especially when the disease is clinically unsuspected. The outbreak of the COVID-19 pandemic may occur in waves and should these waves occur, it will become increasingly important to identify imaging findings suggestive of COVID-19 in a variety of clinical settings, Dr. Paul Chang, professor of radiology at the University of Chicago, said in a statement. Aidocs ability to detect and triage patients with incidental findings associated with COVID-19 acts as another layer of protection as the disease may continue to circulate in the months to come.

Aidoc got its start in 2016, when veterans of the Israeli Defense Force put their heads together to create an AI platform targeting health care verticals. The startups diagnostics toolset, which doesnt require dedicated hardware, runs continuously in an on-premises virtual machine and ingests scans from picture archiving and communication and radiological information systems. It deidentifies these and sends them onto Aidocs cloud, where algorithms identify and highlight abnormalities before returning the images to radiology workstations for reidentification.

Aidoc offers tests for intracranial hemorrhages and spinal fractures and several chest exams for pulmonary embolisms, pneumothorax, rib fractures, and lung nodules. (All excepting the chest tests, which are limited by U.S. and EU law to investigational use, have CE markings indicating that they conform with health, safety, and environmental protection standards for products sold within most of Europe.) Abdominal tests for free air, dissections, and aneurysms are currently in development.

Aidoc claims that its products can reduce report turnaround times by up to 60.1%, and says that theyve so far analyzed over 1.2 million scans, detected over 360,000 signs of disease, and helped to prioritize more than 150,000 cases across over 300 inpatient and outpatient clinics, level 1 trauma centers, imaging centers, and teleradiology facilities.

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FDA permits use of Aidocs AI to detect incidental findings associated with COVID-19 - VentureBeat

Goal #1: Support a Range of Applications

An AI platform doesnt just need to support TensorFlow or even just the model development workloads. It needs to provide testing pipelines, versioning, sandbox environments, monitoring, and more.

For example, you might start creating Kubernetes clusters for AI workloads. That cluster will run a wide set of applications that need access to a variety of datasets and compute hardware and likely even a variety of protocols.

Like with any platform hosted by IT and DevOps teams, an AI platform should support application scalability and resiliency. And, optimally, data scientists should have self-serve access to new environments.

Without a cohesive plan to support the production pipeline as a unified project, individual application silos often become inefficient, unscalable, and fragile.

Step back and ask, How can we make this set of disparate workloads as easy to manage and to scale as possible?

If youre an IT leader, you have an incredible opportunity. The success of your companys AI-fueled ambitions requires you to enable developers in a new way.

Get in front of the productionalization crisis by making architectural choices that will centralize AI infrastructure consolidating people, process and technology.

On the storage side, use the same centralized storage underneath all of the applications in the platform. For example, Pure Storages FlashBlade is great at handling all different IO patterns and has performant access for both file and object workloads, which means its well suited for any of these components.

Likewise, NVIDIAs DGX A100 brings consolidation to the compute hardware. With DGX A100, NVIDIA consolidated what used to be three separate silos of legacy compute infrastructure, each sized and designed for supporting only one specific workload: training or inference or analytics. DGX A100 supports all of these workflows using just one universal system type.

Now you have just two building blocks to manage one for storage and one for compute. This infrastructure simplicity is what lowers the threshold to be able to get models into production; theres already a place where new workloads can run. With the AIRI reference architecture from Pure Storage and NVIDIA, you can now support the end to end AI lifecycle from development to deployment on one elastic infrastructure.

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How IT teams can set the right foundation for AI projects - Medium

By Cuneyt Buyukbezci

Increasing automation and digitization is inevitable. More companies are transferring their operations to IT systems, and more of these operations are being automated.

However, what isnt inevitable is the rise in IT failures and periods of downtime that digitization and automation entail. Businesses are losing billions of dollars per year from IT downtime.

Fortunately, the increasing use of AI-based predictive analytics can root out problems before they even arise.

First, lets just get a firm handle on the scale of the problem, and just how much of the economy is being digitized and automated. Almost 80 percent of companies in the United States are in the process of digital transformation, meaning that 80 percent of American businesses are turning increasingly to IT systems to handle and execute various aspects of their work. And theyre pumping lots of money into this process of change: According to a recent study from Reports and Data, the global digital transformation market was valued at $261.9 billion in 2018, while its estimated to reach $1.051 trillion by 2026.

In other words, massive shifts are taking place around the world as businesses come to depend more on IT systems and digital platforms. At the same time, much of the functioning of these systems and platforms is being automated. A report from Deloitte published this year found that 58 percent of organizations globally have introduced some form of automation into their work processes, while the number of companies implementing automation at scale has doubled over the last year. This is another monumental change, indicating that as companies move to IT systems, theyre also moving towards automating much of what these systems do.

This is all very exciting, but unfortunately, this shift has caused an exponential rise in opportunities for IT failures and downtime. As more processes are put on some kind of computer system, and as more of these processes are executed by algorithms, then inevitably more chances for faults and breakdowns arise, particularly as staff are ill-equipped to monitor everything an increasingly automated system does. Indeed, estimates of the costs of downtime in lost revenue went from $26.5 billion globally in 2011to $700 billion in 2016 (and only for North American firms).

Things are getting out of hand, and one of the main reasons why many firms havent been able to solve this challenge is because theyve approached it with the wrong mentality. Generally, theyve been developing and using tools to detect IT problems as and when they appear. This might sound fine at first glance, but waiting for problems to arise can be dangerous, since they can sometimes take a long time to resolve.

For instance, the UK Parliaments Treasury Committee released a report in October complaining about the spate of IT bank failures that had been occurring in Britain over the last few years, and about how these had left millions of customers locked out of their accounts as the institutions concerned struggled to restore their systems. One of the worst examples of this occurred in 2018 when an IT outage affecting Lloyds Bank resulted in 1.9 million customers being locked out of their accounts for weeks, with the underlying problems taking several months to completely resolve.

To avoid such disasters, businesses should really take a proactive approach to their IT systems. Specifically, they need to focus on preventing problems from materializing in the first place, so that they arent left with periods of downtime that end up hurting their bottom lines. Artificial intelligence is the key to achieving this.

AI-based detection platforms are capable of monitoring IT systems in real-time, checking for early signs of potential failures. To take one example, my company Appnomic has managed to handle 250,000 severe IT incidents for our clients with AI, which equals more than 850,000 man-hours of work.

By harnessing machine learning, such platforms can use past data to learn how problems typically develop, enabling a company to step in before anything unfortunate occurs. In 2017, Gartner coined the term artificial intelligence systems for IT operations (AIOps) to describe this kind of AI-driven predictive analysis, and the market research firm believes that the use of AIOps will grow considerably over the next few years. In 2018, only 5 percent of large enterprises are using AIOps, but the firm estimates that by 2023 this figure is set to rise to 30 percent.

This growth will be driven by the fact that several benefits come from the application of machine learning and data science to IT systems. Aside from detecting likely problems before they occur, AI can significantly reduce false alarms, in that it can gain a more reliable grasp of what actually leads to failures than previous technologies and human operators. On top of this, it can detect anomalies that wont necessarily lead to failures or downtime, but that may be making an IT system less efficient.

This is why AI analytics will make IT systems more resilient and robust overall. And as more companies migrate to AIOps and related platforms they will create a snowball effect, forcing their competitors to either join the race to avoid unnecessary downtime or be left behind. And it makes perfect sense that, as automation in IT systems increases, there should be a parallel increase in automated predictive analytic systems. Because as software eats the world and we humans become less central to our own jobs, its only AI that can keep up with AI.

About the author

Cuneyt Buyukbezci is the Chief Marketing Officer of Appnomic. Cuneyt has a history of working at big enterprises running marketing, product strategy, and sales leadership at HP Software, Sun Microsystems, and CA Technologies. Hes currently helping enterprises learn how they can adopt a preemptive approach to IT management, instead of firefighting after systems fail.

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How AI and predictive analytics eliminate costly IT downtime - TechTalks

OK, perhaps the events of the last few months have only moved the needle a bit, but the fact remains that we take a heck of a lot of pictures of ourselves with our hands covering our faces. And it turns out thats a fairly serious problem for facial recognition.

Yes, I had fun attempting this 47 times at my local coffee shop.

If youve ever tried to use a filter in Snapchat or Facebook, you might have noticed how easy it is to throw everything off. Hands tend to be a particular pain for this type of computer vision because they share so many properties with faces color, texture, etc.

A team of researchers from the University of Central Florida andCarnegie Mellon University dedicated an entire paper to dealing with the problems facial occlusion poses to AI. The team of four created a method for synthesizing images of hands obstructing faces. This data can be used to improve the performance of existing facial recognition models and potentially even enable more accurate recognition of emotion.

Typically, facial recognition models work by identifying landmarks. Though not entirely explicit, the geometric relationship between your mouth and eyes is critical for recognition.

Despite the fact that we occlude our faces with our hands regularly, very little research has been done on performing facial recognition with hand occlusion. There just isnt very much data available, cleanly organized and naturalized images to satiate the thirst of deep learning models.

We were building models and we noticed that visual models were failing more than they should, Behnaz Nojavanasghari, one of the researchers explained to me in an interview. This wasrelated to facial occlusion.

This is why creating a pipeline for image synthesis is so helpful. By masking hands away from their original images, they can be applied to new images that lack occlusion. This gets to be pretty tricky because the placement and appearance of the hands have to look natural after digital transplant.

Synthesized images are color corrected, scaled and oriented to emulate a real image. One of the benefits of this approach is that it creates a data set containing the exact same image with both occlusion and no occlusion.

The downside is that the research team had no real, non-synthisiszed, data set to compare to. Though Nojavan was confident that even if the generated images are not perfect, theyre good enough to push research forward in the niche space.

Hands have a large degree of freedom, Nojavanasghari added. If you want to do it with natural data its hard to make people do all kinds of gestures. If you train people to do gestures, it is not naturalistic.

When hands cover the face they create uncertainty and remove critical information that can typically be extracted from a facial image. But hands also add information. Different hand placement can express surprise, anxiety and a complete withdrawal from the world and its disfunction.

Startups like Affectiva make it their business to interpret emotion from images. Improving facial recognition, and emotional recognition in particular, has broad applications in advertising, user research and robotics to name a few. And it might just make Snapchat a tad less likely to mistake your hand for your face.

Of course it also might help machine intelligence keep up with the official gesture of 2017 thefacepalm.

Hat tip:Paige Bailey

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Our collective facepalm has gotten so bad, AI researchers are dedicating time to it - TechCrunch

Friend of mine told me the other day that his good friend, Tee Green, was using artificial intelligence A.I. to help him hunt Whitetail. I asked him to explain the intersection between artificial intelligence and white tailed deer hunting. Well, that's the genesis really is a derivative of a business partner of mine and a couple of us that have been building software companies together for the last 20 years that are just passionate bow hunters. So we've had leases around the country and we own some of our own property as well. When the game cameras came out we said This is the greatest thing in the world. And we had a property that had five game cameras and we had a property with 60 game cameras. And then you start realizing that each camera is taking about eight hundred pictures a week and there's only a handful of pictures in there that are worth anything. And so we were spending enormous amounts of time scrolling through and cataloging pictures and Microsoft Excel Word documents trying to cross-reference them from year to year and the amount of time it spends just to go through was just incredible. So over the last several years we started with the artificial intelligence and machine learning in a way, we understand software and building software companies. We've been able to take the basic background of sorting images, using data and machine learning capability. So we load whether it's SD cards, whether it's cell camera, it doesn't really matter to us where the images come from.

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AI and deer - AG INFORMATION NETWORK OF THE WEST - AGInfo Ag Information Network Of The West

Hot Hardware

Facebook Shuts Down AI System After It Continued To ... Hot Hardware This is why we can't have nice things. Did you ever have the feeling that people are talking behind your back, but you can't quite make out what they're saying? Facebook's AI bots are communicating in a language we don't understandTechly Facebook Shut Down AI After It Invented Its Own LanguageThe Epoch Times Facebook pulls plug on AI bots after they start inventing their own languageGlobalnews.ca WESH Orlando -DATAQUEST -The Mary Sue -Fast Co Design all 28 news articles »

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Facebook Shuts Down AI System After It Continued To ... - Hot Hardware

Flower Boutique

SEO, or Search Engine Optimization, has become increasingly popular over the past few years among e-commerce businesses exploring new marketing efforts - primarily due to the inconsistency and unprofitability of modern-day paid advertising. This strategy seeks to help businesses rank higher in search engines (like Google) to increase both the quality and quantity of traffic to their website.

The problem with most advertising strategies is that as you scale a campaign up, your costs scale up too, and profitability inevitably dwindles. Thats not even taking into consideration the day-to-day volatility of these campaigns.

The key difference between these types of paid advertising campaigns and SEO is that your costs stay more or less the same while traffic and revenue scale up. This leads to profit margins you may have previously thought were unattainable for a specific business.

SEO takes into consideration user experience, relevancy, authority, and more to determine where exactly your website ranks in Google.

All of these things combine to make up what is known as the Google Algorithm - a piece of artificial intelligence that serves as the deciding factor in whether you gain an explosion in traffic from page 1 rankings, or your website gets buried to the depths of the SERPs.

The idea behind the algorithm is simple - the AI seeks to present the most relevant, high quality content possible to Googles users - aka searchers. When users find exactly what theyre looking for on Google, they have a good experience - websites that help users have a good experience get rewarded with higher rankings.

However, mastering this Googles AI takes expertise. Keval Shah is the founder and CEO of Inbound Pursuit - an SEO agency focused primarily on e-commerce businesses looking to top the rankings in their respective niche.

In his previous role at a social media agency, Shah discovered that most paid marketing campaigns didnt produce the ROI they were expected to. These campaigns typically had lower profit margins and conversion rates than expected, along with inconsistent sales.

This is where SEO comes in to balance the scale. Conversion rates are way way higher, because youre dealing with people already looking for the types of offerings you have. Profit margins soar as your traffic scales, because your costs stay the same, while revenue rises. And while the algorithm does change from time to time - volatility is minimal on Google when SEO is done correctly.

Keval states, "Ad costs are always rising, resulting in less frequent sales and lower profit margins - especially as you scale. More recently, instability from iOS 14 has caused many businesses to see their revenue cut in half, as they put all their eggs in one basket - paid traffic. SEO is the solution to the modern businesses marketing woes in 2021.

According to Keval Shah, finding the right agency is the most important factor in whether you will be able to master Googles AI.

Keval Shah

So many business owners have been burned by SEO agencies that dont know what theyre doing, Keval says. This is one of my biggest motivators - changing the narrative around what you should expect when working with an SEO agency.

Shah believes that SEO is something that needs to be personalized for each business. Because the conditions of Googles AI are always changing, Shah believes it is crucial to constantly test and implement new tactics to make sure your campaign is at the leading edge of whats possible with SEO.

However, dont get bogged down too much by the newest version of the algorithm because the goal is always the same - create a useful, relevant website with high quality content that users will actually benefit from.

As more businesses discover the huge benefits of mastering Googles AI and SEO and realize that paid advertising is becoming an unprofitable rat race in 2021, it is likely that more companies will begin supplementing paid advertisements with SEO strategy.

Keep an eye on this trend, your business will not want to be left behind when it comes to adopting this innovative AI-forward marketing strategy.

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For Small Businesses, Mastering Googles AI Is A Better Investment Than Paid Ads - Heres Why - Forbes

BURLINGTON, Mass., June 10, 2020 (GLOBE NEWSWIRE) -- Cerence Inc. (NASDAQ: CRNC), AI for a world in motion, today announced its enhanced Cerence Studio, an innovative web-based developer platform that allows OEMs and their technology partners to design and develop custom voice domains, creating their own uniquely branded experience for drivers.

As the use of in-car voice assistants explodes, the technology has become an innovative point of differentiation amongst OEMs. Carmakers want the freedom to offer distinct and compelling voice solutions that redefine the driver experience and create a deep connection to their brand for years to come. Cerence Studio provides access to Cerences intuitive natural language understanding (NLU) technology and advanced conversational dialog system to develop custom domains that take automotive voice assistants to the next level.

At HARMAN, we have been using Cerence Studio to explore new opportunities and innovate our HARMAN Ignite solutions, Sandip Ranjhan, SVP and General Manager, HARMAN. We find the tool to add incredible value through its ease of use from initial concept to production and through access to Cerences deep expertise building cutting-edge voice assistants.

Cerence Studio is a comprehensive development environment that goes beyond voice recognition, NLU and dialog tools to allow developers to collaboratively manage the lifecycle of a project. From ideation to production, Cerence Studio provides access to tools, tutorials, technology and expertise from Cerences decades of experience in developing best-in-class in-car voice assistants.

Cerence Studios key capabilities and benefits include:

As the worlds premier provider of in-car voice assistants, Cerence puts decades of industry experience and innovation at a developers fingertips with Cerence Studio, shared Stefan Ortmanns, EVP and General Manager, Core Products for Cerence. Leveraging our deep expertise, we offer OEMs the flexibility to build highly specialized, customizable domains for their brand whether its creating chatbots, jokes and games, or even the car manual that deliver the branded experience they are looking for to set them apart from their competition.

For more about Cerence Studio, visit http://www.cerence.com/solutions/cerence-studio. To learn more about Cerence, visit http://www.cerence.com, and follow the company on LinkedIn and Twitter.

About Cerence Inc.Cerence (NASDAQ: CRNC) is the global industry leader in creating unique, moving experiences for the automotive world. As an innovation partner to the worlds leading automakers, it is helping transform how a car feels, responds and learns. Its track record is built on more than 20 years of knowledge and more than 325 million cars on the road today. Whether its connected cars, autonomous driving or e-vehicles, Cerence is mapping the road ahead. For more information, visit http://www.cerence.com.

Contact InformationKate HickmanCerence Inc. Tel: 857-239-0131Email: Kate.Hickman@cerence.com

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Cerence Studio Puts Custom Voice-Driven AI Innovations into the Hands of Developers Around the World - GlobeNewswire

Slide: 1 / of 2. Caption: Numerai

Slide: 2 / of 2. Caption: Caption: Richard CraibNumerai

Wall Street is a competition, a Darwinian battle for the almighty dollar. Gordon Gekko said that greed is good, that it captures the essence of the evolutionary spirit. A hedge fund hunts for an edge and then maniacally guards it, locking down its trading data and barring its traders from joining the company next door. The big bucks lie in finding market inefficiencies no one else can, succeeding at the expense of others. But Richard Craib wants to change that. He wants to transform Wall Street from a cutthroat competition into a harmonious collaboration.

This morning, the 29-year-old South African technologist and his unorthodox hedge fund, Numerai, started issuing a new digital currencykind of. Craibs idea is so weird, so unlike anything else that has preceded it, that naming it becomes an exercise in approximation. Inspired by the same tech that underpins bitcoin, his creation joins a growing wave of what people in the world of crypto-finance call digital tokens, internet-based assets that enable the crowdsourcing of everything from venture capital to computing power. Craib hopes his particular token can turn Wall Street into a place where everyones on the same team. Its a strange, complicated, and potentially powerful creation that builds on an already audacious arrangement, a new configuration of technology and money that calls into question the markets most cherished premise. Greed is still good, but its better when people are working together.

Based in San Francisco, Numerai is a hedge fund in which an artificially intelligent system chooses all the trades. But its not a system Craib built alone. Instead, several thousand anonymous data scientists compete to create the best trading algorithmsand win bitcoin for their efforts. The whole concept may sound like a bad Silicon Valley joke. But Numerai has been making trades in this way for more than a year, and Craib says its making money. Its also attracted marquee backers like Howard Morgan, a founder of Renaissance Technologies, the wildly successful hedge fund that pioneered an earlier iteration of tech-powered trading.

The system is elegant in its way: Numerai encrypts its trading data before sharing it with the data scientists to prevent them from mimicking the funds trades themselves. At the same time, the company carefully organizes this encrypted data in a way that allows the data scientists to build models that are potentially able to make better trades. The crowdsourced approach seems to be workingto a point. But in Craibs eyes, the system still suffers from a major drawback: If the best scientist wins, that scientist has little incentive to get other talented colleagues involved. The wisdom of the crowd runs up against Wall Streets core ethos of self-interest: make the most money for yourself.

Thats where Craibs new token comes in. Craib and company believe Numerai can become even more successful if it can align the incentives of everyone involved. They hope its new kind of currency, Numeraire, will turn its online competition into a collaborationand turn Wall Street on its head in the process.

In its first incarnation, Numerai was flawed in a notable way. The company doled out bitcoin based on models that performed successfully on the encrypted test data before the fund ever tested them on the live market. That setup encouraged the scientists to game the system, to look out for themselves rather that the fund as a whole. It judged based on what happened in the past, not on what will happen in the future, says Fred Ehrsam, co-founder of marquee bitcoin company Coinbase and a Wall Street veteran.

But Craib feels the system was flawed in another waythe same way all of Wall Street is flawed. The data scientists were still in competition. They were fighting each other rather than fighting for the same goal. It was in their best interest to keep the winnings to themselves. If they spread the word, the added competition could cut into their winnings. Though the scientists were helping to build one master AI, they were still at odds. The fund and its creators were at cross-purposes.

Why is tech positive-sum and finance zero-sum? Richard Craib

Today, to fix that problem, Numerai has distributed Numeraire1,000,000 tokens in allto 12,000 participating scientists. The higher the scientists sit on the leaderboard, the more Numeraire they receive. But its not really a currency they can use to pay for stuff. Its a way of betting that their machine learning models will do well on the live market. If their trades succeed, they get their Numeraire back as well as a payment in bitcoina kind of dividend. If their trades go bust, the company destroys their Numeraire, and they dont get paid.

The new system encourages the data scientists to build models that work on live trades, not just test data. The value of Numeraire also grows in proportion to the overall success of the hedge fund, because Numerai will pay out more bitcoin to data scientists betting Numeraire as the fund grows. If Numerai were to pay out $1 million per month to people who staked Numeraire, then the value of Numeraire will be very high, because staking Numeraire will be the only way to earn that $1 million, Craib says.

Its a tricky but ingenious logic: Everyone betting Numeraire has an incentive to get everyone else to build the best models possible, because the more the fund grows, the bigger the dividends for all. Everyone involved has the incentive to recruit yet more talenta structure that rewards collaboration.

Whats more, though Numeraire has no stated value in itself, it will surely trade on secondary markets. The most likely buyers will be successful data scientists seeking to increase their caches so they can place bigger bets in search of more bitcoin rewards. But even those who dont bet will see the value of their Numeraire grow if the fund succeeds and secondary demand increases. As it trades, Numeraire becomes something kind of like a stock and kind of like its own currency.

For Craib, a trained mathematician with an enormous wave of curly hair topping his 6-foot-4-inch frame, the hope is that Numeraire will encourage Wall Street to operate more like an open source software project. In software, when everyone shares with everyone else, all benefit from the collaboration: The software gets better. Google open sourced its artificial intelligence engine, for instance, because improvements made by others outside the company will make the system more valuable for Google, too.

Why is tech positive-sum and finance zero-sum? Craib asks. The tech companies benefit from network effects where people behave differently because they are trying to build a network, rather than trying to compete.

Craig and company built their new token atop Ethereum, a vast online ledgera blockchainwhere anyone can build a bitcoin-like token driven by a self-operating software program, or smart contract. If it catches on the way bitcoin has, everyone involved has the incentive to (loudly) promote this new project and (manically) push it forward in new ways.

But getting things right isnt easy. The risk is that the crypto-economic model is wrong, says Ersham, Tokens let you set up incentive structures and program them directly. But just like monetary policy at, say, the Federal Reserve, its not always easy to get those incentive structures right.

In other words, Craibs game theory might not work. People and economies may not behave like he assumes they will. Also, blockchains arent hack-proof. A bug brought down the DAO, a huge effort to crowdsource venture capital on a blockchain. Hackers found a hole in the system and made off with $50 million.

Craib may also be overthinking the situation, looking for complex technological solutions to solve a problem that doesnt require anything as elaborate as Numeraire. Their model seems overly complicated. Its not clear why they need it, says Michael Wellman, a University of Michigan professor who specializes in game theory and new financial services. Its not like digital currency has magical properties. Numerai could try a much more time-honored approach to recruiting the most talented data scientists, Wellman says: pay them.

After today, Craib and the rest of Wall Street will start to see whether something like Numeraire can truly imbue the most ruthless of markets with a cooperative spirit. Those thousands of data scientists didnt know Numeraire was coming, but if the network effects play out like Craib hopes they will, many of those scientists have just gotten very, very rich. Still, that isnt his main purpose. Craibs goals are bigger than just building a hedge fund with crowdsourced AI. He wants to change the very nature of Wall Streetand maybe capitalism. Competition has made a lot of people wealthy. Maybe collaboration could enrich many more.

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An AI Hedge Fund Created a New Currency to Make Wall Street Work Like Open Source - WIRED

Artificial Intelligence acts as a potential key technology of dystopian future concepts, social control, and autocratic world power fantasies. It is gradually finding its way to the public and private board room discussions and government policies. Even countries like Germany, which were lagging in the AI race, have gone through tremendous change in the past few years. According to PwC research, by 2030, Germany alone shall have Gross Domestic Product (GDP) up by 11.3% and generate 430 billion due to AI. And by percentage, this potential is more than most of the other European Nations. This makes the country as Europes largest economy, with a thriving market and high potential for new to market brands. The study also that industries like healthcare, energy, and the auto industry will benefit from significant productivity gains by adopting AI applications.

While Germany is currently at the forefront of AI in Europe, research and innovative projects have also commenced in the Cyber Valley. The goal is to further the mission to develop increasingly sophisticated machines with extensive capabilities and boost R&D in AI. Founded just four years ago by the Max Planck Institute for Intelligent Systems (MPII) together with auto groups Bosch, Daimler, BMW, and Porsche, the cluster had also secured 1.25 million investment from Amazon for research partnership. The main motive behind this initiative is to leverage AI to make theGerman industries, services, and products even better. Germany is also striving to bring the digital revolution through Industry 4.0, which was also mentioned in the AI strategy of 2018. The strategy report further expresses that the country shall expand its strong position and rise to be a global leader in AI on the grounds of ethics and legal terms too. It also intends to use AI to promote social participation, freedom of action, and self-determination for citizens and foster the sustainable development of the society. To achieve this goal, the Federal Government first allocated a total of 500 million to beef up the AI strategy for 2019 and further anticipates matching funds from the private sector and other the federal states, therefore bringing the total investment to 6 billion.

Meanwhile, the emphasis is also made on improving data sharing facilities by providing open access to governmental data. The government is also working to build a reliable data and analysis infrastructure based on cloud platforms and upgraded storage and computing capacity. These measures are crucial and necessary as, without data, AI innovations cannot be used to solve the bottlenecks and other issues faced by different industries in their quest for AI adoption. Recently, Germany is looking for ways to tighten data security. It is calling for a more concrete definition when data records must be stored on a mandatory basis. At the European Union, it has also requested for developing a new classification scheme together with the member states.

On the business front, tests are carried to maximize the use of collaborative AI robots and link augmented reality technology to AI-based production planning systems. Major automobile behemoths Volkswagen, BMW, and Daimler, are investing heavily in modern, AI-controlled factories. They are working on solutions for assisted and autonomous driving, intelligent operating systems, entertainment systems, and navigation systems at their German R&D centers.

Germany is also growing as a preferred hub for startups focusing on AI and its applications like machine learning, deep learning, computer vision, predictive analysis, and so on. Further, it has the most active corporate venture investors in Europe (91% of all non-IPO exitsin 2019 were related to corporates). The most common areas of focus for these AI startups are software development, image recognition, customer support and communication, and marketing and sales. These five categories are found to constitute around 48% of German AI startups. Currently, Berlin is the fourth largest global AI hub, following Silicon Valley of the USA, London, and Paris. So, it is high time that companies take Germany as an upcoming nation in global AI leadership and start investing or collaborating with it.

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A Brief Outlook on the Artificial Intelligence landscape in Germany - Analytics Insight

There are myriad articles on artificial intelligence and its application in business. As AI continues to grow and permeate seemingly every aspect of business, its important to cut through the noise and focus on where AI fits in your organization and how to best implement it.

I'm the founder and CEO of an AI-based customer relationship management platform. Through this experience, I've learned a few ways leaders can determine their own approach to AI.

A Brief Overview Of AI Application In B2B

Broadly speaking, AI is a branch of computer science concerned with replicating human intelligence in machines. Depending on whether you run a business-to-consumer or business-to-business company, you might find some types of AI more relevant to your business than others.

In B2B, AI is all about data and analysis to make better-informed decisions. For example, if you have enough sales and customer data, you can use predictive analytics to figure out your ideal customer profile and/or potential customer base and adjust your marketing strategy and campaigns accordingly.

In more technical terms, AI applications in B2B can be broken into three types of machine learning: supervised, unsupervised and reinforcement.

In the case of supervised learning, you or someone with business intelligence skills feeds the data to the learning algorithm (a statistics algorithm) and sets a goal (what you want to get to or what youre looking for). The machine then tries to match that goal.

In unsupervised learning, the algorithm looks at the data and searches for patterns. As the name suggests, there are no instructions given prior to the analysis. For example, it can look at your customer data and decide that you have a cluster of customers in the manufacturing industry that looks really promising.

Finally, in reinforcement learning, which is more advanced, the algorithm looks at the data and comes up with a set of conclusions. You dont provide a predefined dataset or any guidance; its more of a trial-and-error method. You look at the results and tell it whether the conclusions are correct, and it continues to reinforce the right steps to get to an endpoint.

How can AI benefit your business?

For businesses that collect a lot of customer data at every point, being able to use AI to derive meaning from that data can help get ahead of the competition. You can spot trends early and identify areas where you're losing revenue or where you could potentially gain revenue. You can then make data-driven decisions and quickly adapt to changes.

AI can also impact your CRM system and team productivity by helping identify leads, building effective nurture campaigns or personalizing the customer experience. (A number of companies, my own included, offer CRM and marketing AI solutions.)

Although theres some concern about AI replacing jobs, I believe there's an opportunity for AI to help, not hinder, the performance of marketers, salespeople and customer service representatives. However, taking steps to introduce it successfully is critical.

How can you introduce AI in your business?

Make sure you are clear on where in the business you want to use AI and what you hope it will solve for you. Keep in mind that you need to have enough data to make your AI investment worth it. Once youve done that, train your employees on how itll work. Remember: Its not a black box.

When introducing any new technology, its always good to begin with a small project and work from there. Start with a hypothesis and a goal, and at the end, analyze how well you did and if you reached the right conclusions. The first project is really about the journey more than the end goal.

Finally, consider any challenges that might come your way. For example, there are two sides to managing AI expectations. Some people on your team might think its awesome and will solve a lot of problems. Others might get scared, thinking its going to replace their jobs.

Try to address the expectations and concerns of both extremes. AI is not going to solve everything, and in a B2B company, it most likely wont replace jobs. You have to tamp down both the enthusiasm and worries surrounding AI to ensure buy-in before you make it part of your business.

What technology do you need to implement AI for the first time?

You can start by using available cloud computing resources, which can be helpful for small to midsized companies because you dont need to know a lot of underlying methodologies.

Alternatively, you might decide to set up AI technology on-premises. If you go this route, keep in mind that youll need some hefty horsepower and someone with a lot of knowledge of the underlying analytical algorithms and statistics to run through big datasets and get the highest ROI from AI.

Increasingly, I believe its not a question of if, but when you should implement AI in your business. The sooner you figure out your AI approach, the sooner youll start reaping its benefits.

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Tips On Introducing Artificial Intelligence In Your Business - Forbes

A pause button at the bottom of the screen would play a 20-second video clip featuring tall green trees and sounds of birds chirping and is intended help users calm down if things get heated. It plays over the entire video chat, too, and everyone has to take a break. A Chat button on the right pulled up a window for us to interact via text with both Serenity and other participants. There were also options to turn off our mics and cameras. Serenity told us to mute ourselves when not speaking (which I was grateful for because the feedback from seven peoples mics was infuriating).

After the introduction, Serenity asked everyone What are you mirroring now? That was a confusing question to start with, but one of my fellow attendees rephrased it for us. He speculated that it meant what we were thinking about and reflecting on, and we all answered based on that interpretation. At this point, the experience may sound painfully familiar and borderline pointless.

But Serenity went on to ask truly thought-provoking questions, like what wed like to see more of in 2050 or what wed like to not be talking about in that year. Then, it continued prodding, asking about the types of new jobs that would need to be created to facilitate some of our groups declared values and the world we wanted to create. The questions also differ slightly across all the sessions, according to McCarthy. She said that Each session follows an arc and many of the questions are the same, but there's also variation in response to the group discussion and flow.

Beyond the Breakdown

Beyond the Breakdown is about more than just introspection and imagining the future, though. Its core focus is conversation and dialogue otherwise why have you answer these questions with a group of strangers? Whenever it seemed like not everyone had responded, Serenity asked if anybody had more to add.

Learning from others in the conversation was what made the experience illuminating and hopeful. When Serenity asked where we thought people would call home in 2050, my fellow participants answers surprised me. I was thinking of more straightforward answers like, Earth, for example, but others talked about communal living spaces. Some questions were pretty vague, though, like What does care look like in this world, and some members of my group chose to interpret it as healthcare while others took it to mean community care.

Still, seeing how people interpreted and responded to the questions was part of learning about various perspectives. Like Lee said, the sessions offer an opportunity to build something rather than just ingest. Had I only been speaking with Serenity, I would have missed out on the collaborative aspect.

But of course, the quality of your BTB experience hinges on the people you get to interact with. My session was filled with a somewhat biased, self-selecting sample Sundance attendees that had access to a computer and spoke English. That excludes people from different socio-economic backgrounds or other nationalities that didnt converse in English. And while I applaud BTBs built-in accessibility features like live closed-captioning and text-based support, there are plenty of other considerations that still have to be made.

Beyond the Breakdown

That said, the fact that I was speaking with intelligent, seemingly like-minded people was a huge part of why I enjoyed BTB. It left me hopeful that the world isnt filled with angry people who shut down rational discourse, and that there are people committed to building a better future through empathy, sympathy and by listening to others. But I can imagine how my experience would have been completely different had it been filled with people who disagreed on fundamental issues. Sure, theres always the Pause button to cool things down, and anyone who signs up for a session of BTB is most likely going to be open-minded and agreeable to begin with. But Im not sure a 20-second timeout would be enough to cool down a truly heated argument.

Patrick said one of the questions he wanted BTB to answer was, Is it possible for a browser to help us with communal and community care? McCarthy added, What if the browser or the video chat experience itself could be leading you through this process, and what happens if we start to bring AI into that?

I didnt see Serenity step in to calm down a tricky situation since my session mates were all respectful and agreeable. In retrospect, I wish someone in my group had at least pretended to get heated to see how Serenity would have handled things. I like the idea of a neutral AI moderator leading the conversations, since it could appear more objective to participants regardless of their ideological differences. But I do believe that Beyond The Breakdown has an inherent limit: reach. The people we need to be having open-minded and open-hearted conversations in safe spaces with might not be likely or willing to sign up for such a chat. What it does offer to those of us keen on speaking with people around the world though, is a glimmer of hope as we shake off the debris of 2020 and head into the rest of the decade.

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My AI-moderated video chat with strangers gave me hope - Engadget

In the 18th century, Hungarian inventor Wolfgang von Kempelen created a never-before-seen chess-playing machine. The automaton, called the Mechanical Turk, could handle a game of chess against a human player, and pretty well with that: it even defeated Napoleon Bonaparte in 1809, during a campaign in Vienna.

It was eventually revealed that von Kempelen's invention was an elaborate hoax. The machine, in reality, secretly hid a human chess master who directed every move. The Mechanical Turk was destroyed in the mid-19th century; but hundreds of years later, the story provides a telling metaphor for artificial intelligence.

A common narrative that surrounds AI is that the technology has agency. We hear that AI can solve climate change, build smart cities and find new drugs, and less often that in fact, it is a human programmer who is using an AI system to achieve all of those feats. Just like the human chess master hid behind von Kempelen's ingenious mechanism, so do engineers, programmers, and software developers disappear behind the algorithm.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

The relevance of the Hungarian machine is such that Amazon borrowed the name for one of its units one that is often less known than Prime or Fresh. Amazon Mechanical Turk is a division of the company that crowdsources the tedious job of labeling the huge datasets that feed AI systems to millions of remote "Turkers".

"I'm amazed that every four months or so, I catch a Tweet from someone who realizes what Amazon's Mechanical Turk is," Daniel Leufer, Mozilla fellow and technologist, tells ZDNet. "I find it fascinating that Amazon calls a platform designed to mask the human agency behind AI Mechanical Turk. We're not even hiding what we're trying to do here."

Leufer has just put the final touches to a new project to debunk common AI myths, which he has been working on since he received his Mozilla fellowship an award designed for web activists and technology policy experts. And one of the most pervasive of those myths is that AI systems can and act of their own accord, without supervision from humans.

It certainly doesn't help that artificial intelligence is often associated with humanoid robots, suggesting that the technology can match human brains. An AI system deployed, say, to automate insurance claims, is very unlikely to come in the form of a human-looking robot, and yet that is often the portrayal that is made of the technology, regardless of its application.

Leufer calls those "inappropriate robots", often shown carrying out human tasks that would never be necessary for an automaton. Among the most common offenders feature robots typing on keyboards and robots wearing headphones or using laptops.

The powers we ascribe to AI as a result even have legal ramifications: there is an ongoing debate about whether an AI system should own intellectual property (a proposal refuted by the European Patent Office and the UK Intellectual Property Office), or whether automatons should be granted citizenship. In 2017, for instance, Shibuya Mirai became the first chatbot to be granted residency in Tokyo by the Japanese government.

The current representation of AI feeds into the perception that the technology comes in one form, and one form only: a super-powerful system capable of general intelligence that is, of performing intelligently across a range of complex tasks, and eventually completing anything that a human can do.

Although achieving such a sophisticated form of artificial intelligence is not a prospect envisaged by many scientists, it seems to be the narrative that dominates even the highest level of geo-politics. "There is an entire narrative around the race for AI supremacy going on between the US, China and Europe," says Leufer. "That just doesn't make sense."

"If you believe we're headed towards an end-point, where a super-intelligence will grant you technological supremacy, then maybe it makes sense, but that's not the case. This is not a zero-sum game," he continues.

In reality, AI as we know it is still narrow. It can only solve a range of single tasks, and the step up to general intelligence is still far away in the future. But even if the anticipation of super-intelligence is currently unfounded, the consequences of misrepresenting the technology are very real.

Leufer takes the example of facial recognition, which he believes needs to be banned across the EU. The response he got from regulators, he argues, shows a lack of understanding of the technology.

"The idea is that this is a part of AI, and AI is inevitable, so we'll have to adopt it eventually and we better develop it ourselves so it is imbued with European values," says Leufer. "But AI is not just one technology. There are many ways you can use it."

SEE: CIO Jury: 58% of tech leaders say robotics will play a significant role in their industry within the next two years

Becoming a leader in industry robotics doesn't have to go hand-in-hand with developing facial recognition, just because both tap AI-enabled capabilities. It might be less exciting than the prospect of a super-intelligence, but AI is not one huge technology waiting to be cracked. In other words, artificial intelligence is not an all or nothing.

And so, as countries around the world race to develop all potential AI applications, regulation is crucial to make sure that the development of what Leufer calls "creepy stuff" is limited.

He is currently working with German NGO AlgorithmWatch to push for the creation of public registers for AI systems, in which public authorities and governments would have to provide basic information about the ways that they are using the technology, together with risk assessments, and even a way for citizens to contest the application.

"At the moment we're working in the dark, we don't know what's being used," says Leufer. Super-intelligent humanoid robots might still be a long way off, but narrow AI isn't short of issues that need fixing right now.

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We've forgotten the most important thing about AI. It's time to remember it again - ZDNet