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Deciphering Artificial Intelligence in the Future of Information Security – AiThority

December 22, 2019 | Author: admin

Artificial Intelligence (AI) is creating a new frontline in information security. Systems that independently learn, reason and act will increasingly replicate human behavior. Like humans, they will be flawed, but also capable of achieving great things.

AI poses new information risks and makes some existing ones more dangerous. However, it can also be used for good and should become a key part of every organizations defensive arsenal. Business and information security leaders alike must understand both the risks and opportunities before embracing technologies that will soon become a critically important part of everyday business.

Already, AI is finding its way into many mainstream business use cases. Organizations use variations of AI to support processes in areas including customer service, human resources, and bank fraud detection. However, the hype can lead to confusion and skepticism over what AI actually is and what it really means for business and security. It is difficult to separate wishful thinking from reality.

Read More: How AI and Automation Are Joining Forces to Transform ITSM

As AI systems are adopted by organizations, they will become increasingly critical to day-to-day business operations. Some organizations already have, or will have, business models entirely dependent on AI technology. No matter the function for which an organization uses AI, such systems and the information that supports them have inherent vulnerabilities and are at risk from both accidental and adversarial threats. Compromised AI systems make poor decisions and produce unexpected outcomes.

Simultaneously, organizations are beginning to face sophisticated AI-enabled attacks which have the potential to compromise information and cause severe business impact at a greater speed and scale than ever before. Taking steps both to secure internal AI systems and defend against external AI-enabled threats will become vitally important in reducing information risk.

While AI systems adopted by organizations present a tempting target, adversarial attackers are also beginning to use AI for their own purposes. AI is a powerful tool that can be used to enhance attack techniques or even create entirely new ones. Organizations must be ready to adapt their defenses in order to cope with the scale and sophistication of AI-enabled cyberattacks.

Security practitioners are always fighting to keep up with the methods used by attackers, and AI systems can provide at least a short-term boost by significantly enhancing a variety of defensive mechanisms. AI can automate numerous tasks, helping understaffed security departments to bridge the specialist skills gap and improve the efficiency of their human practitioners. Protecting against many existing threats, AI can put defenders a step ahead. However, adversaries are not standing still as AI-enabled threats become more sophisticated, security practitioners will need to use AI-supported defenses simply to keep up.

The benefit of AI in terms of response to threats is that it can act independently, taking responsive measures without the need for human oversight and at a much greater speed than a human could. Given the presence of malware that can compromise whole systems almost instantaneously, this is a highly valuable capability.

The number of ways in which defensive mechanisms can be significantly enhanced by AI provide grounds for optimism, but as with any new type of technology, it is not a miracle cure. Security practitioners should be aware of the practical challenges involved when deploying defensive AI.

Questions and considerations before deploying defensive AI systems have narrow intelligence and are designed to fulfill one type of task. They require sufficient data and inputs in order to complete that task. One single defensive AI system will not be able to enhance all the defensive mechanisms outlined previously an organization is likely to adopt multiple systems. Before purchasing and deploying defensive AI, security leaders should consider whether an AI system is required to solve the problem, or whether more conventional options would do a similar or better job.

Read More: Artificial Intelligence in Restaurant Business

Questions to ask include:

Security leaders also need to consider issues of governance around defensive AI, such as:

AI will not replace the need for skilled security practitioners with technical expertise and an intuitive nose for risk. These security practitioners need to balance the need for human oversight with the confidence to allow AI-supported controls to act autonomously and effectively. Such confidence will take time to develop, especially as stories continue to emerge of AI proving unreliable or making poor or unexpected decisions.

AI systems will make mistakes a beneficial aspect of human oversight is that human practitioners can provide feedback when things go wrong and incorporate it into the AIs decision-making process. Of course, humans make mistakes too organizations that adopt defensive AI need to devote time, training and support to help security practitioners learn to work with intelligent systems.

Given time to develop and learn together, the combination of Human and Artificial Intelligence should become a valuable component of an organizations cyber defenses.

Computer systems that can independently learn, reason and act herald a new technological era, full of both risk and opportunity. The advances already on display are only the tip of the iceberg there is a lot more to come. The speed and scale at which AI systems think will be increased by growing access to big data, greater computing power and continuous refinement of programming techniques. Such power will have the potential to both make and destroy a business.

AI tools and techniques that can be used in defense are also available to malicious actors including criminals, hacktivists and state-sponsored groups. Sooner rather than later these adversaries will find ways to use AI to create completely new threats such as intelligent malware and at that point, defensive AI will not just be a nice to have. It will be a necessity. Security practitioners using traditional controls will not be able to cope with the speed, volume, and sophistication of attacks.

To thrive in the new era, organizations need to reduce the risks posed by AI and make the most of the opportunities it offers. That means securing their own intelligent systems and deploying their own intelligent defenses. AI is no longer a vision of the distant future: the time to start preparing is now.

Read More: How Artificial Intelligence Can Transform Influencer Marketing

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7 tips to get your resume past the robots reading it – CNBC

December 22, 2019 | Author: admin

There are about 7.3 million open jobs in the U.S., according to the most recent Job Openings and Labor Turnover Survey from the Bureau of Labor Statistics. And for many job seekers vying for these openings, the likelihood they'll submit their application to an artificial intelligence-powered hiring system is growing.

A 2017 Deloitte report found 33% of employers already use some form of AI in the hiring process to save time and reduce human bias. These algorithms scan applications for specific words and phrases around work history, responsibilities, skills and accomplishments to identify candidates who match well with the job description.

These assessments may also aim to predict a candidate's future success by matching their abilities and accomplishments to those held by a company's top performers.

But it remains unclear how effective these programs are.

As Sue Shellenbarger reports for The Wall Street Journal, many vendors of these systems don't tell employers how their algorithms work. And employers aren't required to inform job candidates when their resumes will be reviewed by these systems.

That said, "it's sometimes possible to tell whether an employer is using an AI-driven tool by looking for a vendor's logo on the employer's career site," Shellenbarger writes. "In other cases, hovering your cursor over the 'submit' button will reveal the URL where your application is being sent."

CNBC Make It spoke with career experts about how to make sure your next application makes it past the initial robot test.

AI-powered hiring platforms are designed to identify candidates whose resumes match open job descriptions the most. These machines are nuanced, but their use still means very specific wording, repetition and prioritization of certain phrases matter.

Job seekers can make sure to highlight the right skills to get past initial screens by using tools, such as an online cloud generator, to understand what the AI system will prioritize most. Candidates can drop in the text of a job description and see which words appear most often, based on how large they appear within the word cloud.

CareerBuilder also created an AI resume builder to help candidates include skills on an application they may not have identified on their own.

Including transferable skills mentioned in the job description can also increase your resume odds. After all, executives from a recent IBM report say soft skills such as flexibility, time management, teamwork and communication are some of the most important skills in the workforce today.

"Job seekers should be cognizant of how they are positioning their professional background to put their best foot forward," Michelle Armer, chief people officer at talent acquisition company CareerBuilder, tells CNBC Make It. "Since a candidate's skill set will help set them apart from other applicants, putting these front and center on a resume will help make sure you're giving skills the attention they deserve."

It's also worth noting that AI enables employers to source candidates from the entire application system more easily, rather than limiting consideration just to people who applied to a specific role. "As a result," says TopResume career expert Amanda Augustine, "you could be contacted for a role the company believes is a good fit even if you never specifically applied for that opportunity."

When it comes to actually writing your resume, here are seven ways to make sure it looks best for the robots who will be reading it.

Use a text-based application like Microsoft Word rather than a PDF, HTML, Open Office, or Apple Pages document so buzzwords can be accurately scanned by AI programs. Augustine suggests job seekers skip images, graphics and logos, which might not be readable. Test how well bots will comprehend your resume by copying it into a plain text file, then making sure nothing gets out of order and no strange symbols pop up.

Mirror the job description in your work history. Job titles should be listed in reverse-chronological order, Augustine says, because machines favor documents with a clear hierarchy to their information. For each role, prioritize the most relevant information that matches the critical responsibilities and requirements of the job you're applying for. "The bullets that directly match one of the job requirements should be listed first," Augustine adds, "and other notable contributions or accomplishments should be listed lower in a set of bullets."

Include keywords from the job description, such as the role's day-to-day responsibilities, desired previous experience and overall purpose within the organization. Consider having a separate skills section, Augustine says, where you list any certifications, technical skills and soft skills mentioned in the job description.

Quantify performance results, Shellenbarger writes. Highlight ones that involve meeting company goals, driving revenue, leading a certain number of people or projects, being efficient with costs and so on.

Tailor each application to the description of each role you're applying for. These AI systems are generally built to weed out disqualifying resumes that don't match enough of the job description. The more closely you mirror the job description in your application, the better, Augustine says.

Don't place information in the document header or footer, even though resumes traditionally list contact information here. According to Augustine, many application systems can't read the information in this section, so crucial details may be omitted.

Network within the company to build contacts and get your resume to the hiring manager's inbox directly. "While AI helps employers narrow down the number of applicants they will move forward with for interviews," Armer says, "networking is also important."

AI hiring programs show promise at filling roles with greater efficiency, but can also perpetuate bias when they reward candidates with similar backgrounds and experiences as existing employees. Armer stresses hiring algorithms need to be built by teams of diverse individuals across race, ethnicity, gender, experience and other background factors in order to minimize bias.

This is also where getting your resume in front of a human can pay off the most.

"When you have someone on the inside advocating for you, you are often able to bypass the algorithm and have your application delivered directly to the recruiter or hiring manager, rather than getting caught up in the screening process," Augustine says.

Augustine recommends job seekers take stock of their existing network and identify those who may know someone at the companies they're interested in working at. "Look for professional organizations and events that are tied to your industry 10times.com is a great place to find events around the world for every imaginable field," she adds.

Finally, Armer recommends those starting their job hunt review and polish their social media profiles.

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Don't miss: This algorithm can predict when workers are about to quithere's how

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The Machines Are Learning, and So Are the Students – The New York Times

December 22, 2019 | Author: admin

Riiid claims students can increase their scores by 20 percent or more with just 20 hours of study. It has already incorporated machine-learning algorithms into its program to prepare students for English-language proficiency tests and has introduced test prep programs for the SAT. It expects to enter the United States in 2020.

Still more transformational applications are being developed that could revolutionize education altogether. Acuitus, a Silicon Valley start-up, has drawn on lessons learned over the past 50 years in education cognitive psychology, social psychology, computer science, linguistics and artificial intelligence to create a digital tutor that it claims can train experts in months rather than years.

Acuituss system was originally funded by the Defense Departments Defense Advanced Research Projects Agency for training Navy information technology specialists. John Newkirk, the companys co-founder and chief executive, said Acuitus focused on teaching concepts and understanding.

The company has taught nearly 1,000 students with its course on information technology and is in the prototype stage for a system that will teach algebra. Dr. Newkirk said the underlying A.I. technology was content-agnostic and could be used to teach the full range of STEM subjects.

Dr. Newkirk likens A.I.-powered education today to the Wright brothers early exhibition flights proof that it can be done, but far from what it will be a decade or two from now.

The world will still need schools, classrooms and teachers to motivate students and to teach social skills, teamwork and soft subjects like art, music and sports. The challenge for A.I.-aided learning, some people say, is not the technology, but bureaucratic barriers that protect the status quo.

There are gatekeepers at every step, said Dr. Sejnowski, who together with Barbara Oakley, a computer-science engineer at Michigans Oakland University, created a massive open online course, or MOOC, called Learning How to Learn.

He said that by using machine-learning systems and the internet, new education technology would bypass the gatekeepers and go directly to students in their homes. Parents are figuring out that they can get much better educational lessons for their kids through the internet than theyre getting at school, he said.

Craig S. Smith is a former correspondent for The Times and hosts the podcast Eye on A.I.

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Tommie Experts: Ethically Educating on Artificial Intelligence at St. Thomas – University of St. Thomas Newsroom

December 22, 2019 | Author: admin

Tommie Experts taps into the knowledge of St. Thomas faculty and staff to help us better understand topical events, trends and the world in general.

Last month, School of Engineering Dean Don Weinkauf appointed Manjeet Rege, PhD, as the director for the Center for Applied Artificial Intelligence.

Rege is a faculty member, author, mentor, AI expert, thought leader and a frequent public speaker on big data, machine learning and AI technologies. The Newsroom caught up with him to ask about the centers launch in response to a growing need to educate ethically around AI.

Were partnering with industry in a number of ways. One way is in our data science curriculum. There are electives; some students take a regular course, while others take a data science capstone project. Its optional. Students who opt for that through partnership with the industry, companies in the Twin Cities interested in embarking on an AI journey can have several business use cases that they want to try AI out with. In an enterprise, you typically have to seek funding, convince a lot of people; in this case, well find a student, or a team, who will be working on that industry-sponsored project. Its a win-win for all. The project will be supervised by faculty. The company gets access to emerging AI talent, gets to try out their business use case and the students end up getting an opportunity working on a real-world project.

Secondly, a number of companies are looking to hire talent in machine learning and AI. This is a good way for companies to access good talent. We can build relationships, sending students for internships, or even students who work on these capstone projects become important in terms of hiring.

There are also a number of professional development offerings well come out with. We offer a mini masters program in big data and AI. The local companies can come and attend an executive seminar for a week on different aspects of AI. Well be offering two- or three-day workshops on hands-on AI, for someone within a company who would like to become an AI practitioner. If they are interested in getting in-depth knowledge, they can go through our curriculum.

We also have a speaker series in partnership with SAS.

In May well be hosting a data science day, a keynote speaker, and a panel of judges to review projects the data science students are working on (six of which are part of the SAS Global Student Symposium). Theyll get to showcase the work theyve done. That panel of judges will be from local companies.

Everybody is now becoming aware that AI is ubiquitous, around us and here. The ship has already left the dock, so to speak, in terms of AI being around us. The best way to succeed at the enterprise level is to embrace this and make it a business enabler. Its important for enterprises to transform themselves into an AI-first company. Think about Google. It first defined itself as a search company. Then a mobile company. Now, its an AI-first company. That is what keeps you ahead, always.

Being aware of the problems that may arise is so important. For us to address AI biases, we have to understand how AI works. Through these multiple offerings were hoping we can create knowledge about AI. Once we have that we can address the issue of AI bias.

For example, Microsoft did an experiment where it had AI go out on the web, read the literature and learn a lot of analogies. When you went in and asked that AI questions based on, say, what man is to a woman, father is to what? Mother. Perfect. What man is to computer programmer as woman is to what? Homemaker. Thats unfortunate. AI is learning the stereotypes that exist in the literature it was learned on.

There have been hiring tools that have gender bias. Facial recognition tools that work better for lighter skin colors than darker skin colors. Bank loan programs with biases for certain demographics. There is a lot of effort in the AI community to minimize these. Humans have bias, but when a computer does it you expect perfection. An AI system learning is like a child learning; when that AI system learned about different things from the web and different relationships between man and woman, because these stereotypes existed already in the data, the computer just learned from it. Ultimately an AI system is for a human; whenever it gives you certain output, we need to be aware and go back and nudge it in the right direction.

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Nikon Announces Artificial Intelligence (AI) for Predictive Imaging, Image Segmentation and Processing – P&T Community

December 22, 2019 | Author: admin

MELVILLE, N.Y., Dec. 16, 2019 /PRNewswire/ -- Nikon Instruments Inc., innovator of advanced microscope systems, today announced NIS.ai, a powerful image analysis and processing module for NIS-Elements that leverages Deep Learning and Artificial Intelligence. NIS.ai is a suite of new AI-based processing tools that utilizes convolutional neural networks to learn from small training datasets supplied by the user. The training results can then be easily applied to process and analyze huge volumes of data, enabling researchers to increase throughput and expand their application limits.

NIS.ai includes a suite of applications for predictive imaging, image segmentation and processing:

"The application of Deep Learning and AI to biomedical imaging is extremely powerful, and opening up unseen possibilities," said Steve Ross, Ph.D., Director, Products & Marketing, Nikon Instruments Inc. "With NIS.ai, researchers can easily apply deep learning to extract meaningful, unbiased data from large, complex datasets."

To learn more about NIS.ai, visit:https://www.microscope.healthcare.nikon.com/nis-ai

About Nikon Instruments Inc. NikonInstruments Inc. is the US microscopy arm ofNikonHealthcare, a world leader in the development and manufacture of optical and digital imaging technology for biomedical applications. Cutting-edge instruments include microscopes, digital imaging products and software. For more information, visit http://www.microscope.healthcare.nikon.com.

View original content to download multimedia:http://www.prnewswire.com/news-releases/nikon-announces-artificial-intelligence-ai-for-predictive-imaging-image-segmentation-and-processing-300975257.html

SOURCE Nikon Instruments Inc.

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Zebra Medical Vision Announces Agreement With DePuy Synthes to Deploy Cloud Based Artificial Intelligence Orthopaedic Surgical Planning Tools -…

December 22, 2019 | Author: admin

KIBBUTZ SHEFAYIM, Israel--(BUSINESS WIRE)--Zebra Medical Vision, the deep learning medical imaging analytics company, announces today a global co-development and commercialization agreement with DePuy Synthes* to bring Artificial Intelligence (AI) opportunities to orthopaedics, based on imaging data.

Every year, millions of orthopaedic procedures worldwide use traditional two-dimensional (2D) CT scans or MRI imaging to assist with pre-operative planning. CT scans and MRI imaging can be expensive, and CT scans are associated with more radiation and are uncomfortable for some patients. Zebra-Meds technology uses algorithms to create three-dimensional (3D) models from X-ray images. This technology aims to bring affordable pre-operative surgical planning to surgeons worldwide without the need for traditional MRI or CT-based imaging.

We are thrilled to start this collaboration and have the opportunity to impact and improve orthopaedic procedures and outcomes in areas including the knee, hip, shoulder, trauma, and spine care, says Eyal Gura, Co-Founder and CEO of Zebra Medical Vision. We share a common vision surrounding the impact we can have on patients lives through the use of AI, and we are happy to initiate such a meaningful strategic partnership, leveraging the tools and knowledge we have built around bone health AI in the last five years.

This technology is planned to be introduced as part of DePuy Synthes VELYS Digital Surgery solutions for pre-operative, operative, and post-operative patient care.

Read more on Zebra-Meds blog: https://zebramedblog.wordpress.com/another-dimension-to-zebras-ai-how-we-impact-the-orthopedic-world

About Zebra Medical VisionZebra Medical Visions imaging analytics platform allows healthcare institutions to identify patients at risk of disease and offer improved, preventative treatment pathways, to improve patient care. The company is funded by Khosla Ventures, Marc Benioff, Intermountain Investment Fund, OurCrowd Qure, Aurum, aMoon, Nvidia, Johnson & Johnson Innovation JJDC, Inc. (JJDC) and Dolby Ventures. Zebra Medical Vision has raised $52 million in funding to date, and was named a Fast Company Top-5 AI and Machine Learning company. Zebra-Med is a global leader in AI FDA cleared products, and is installed in hospitals globally, from Australia to India, Europe to the U.S, and the LATAM region.

*Agreement is between DePuy Ireland Unlimited Company and Zebra Medical Vision.

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Artificial intelligence predictions for 2020: 16 experts have their say – Verdict

December 22, 2019 | Author: admin

2019 has seen artificial intelligence and machine learning take centre stage for many industries, with companies increasingly looking to harness the benefits of the technology for a wide range of use cases. With its advances, ethical implications and impact on humans likely to dominate conversations in the technology sector for years to come, how will AI continue to develop over the next 12 months?

Weve asked experts from a range of organisations within the AI sphere to give their predictions for 2020.

In both the private and public sectors, organisations are recognising the need to develop strategies to mitigate bias in AI. With issues such as amplified prejudices in predictive crime mapping, organisations must build in checks in both AI technology itself and their people processes. One of the most effective ways to do this is to ensure data samples are robust enough to minimise subjectivity and yield trustworthy insights. Data collection cannot be too selective and should be reflective of reality, not historical biases.

In addition, teams responsible for identifying business cases and creating and deploying machine learning models should represent a rich blend of backgrounds, views, and characteristics. Organisations should also test machines for biases, train AI models to identify bias, and consider appointing an HR or ethics specialist to collaborate with data scientists, thereby ensuring cultural values are being reflected in AI projects.

Zachary Jarvinen, Head of Technology Strategy, AI and Analytics, OpenText

A big trend for social media this year has been the rise of deepfakes and were only likely to see this increase in the year ahead. These are manipulated videos that are made to look real, but are actually inaccurate representations powered by sophisticated AI. This technology has implications for past political Facebook posts. I believe we will start to see threat actors use deepfakes as a tactic for corporate cyberattacks, in a similar way to how phishing attacks operate.

Cyber crooks will see this as a money-making opportunity, as they can cause serious harm on unsuspecting employees. This means it will be vital for organisations to keep validation technology up-to-date. The same tools that people use to create deepfakes will be the ones used to detect them, so we may see an arms race for who can use the technology first.

Jesper Frederiksen, VP and GM EMEA, Okta

When considering high-volume, fast turnaround hiring efforts, its often impossible to keep every candidate in the loop. Enter highly sophisticated artificial intelligence tools, such as chatbots. More companies are now using AI programs to inform candidates quickly and efficiently on where they stand in the process, help them navigate career sites, schedule interviews and give advice. This is significantly transforming the candidate experience, enhancing engagement and elevating overall satisfaction.

Chatbots are also increasingly becoming a tool for employees who wish to apply for new roles within their organisation. Instead of trying to work up the nerve to ask HR or their boss about new opportunities, employees can interact with a chatbot that can offer details about open jobs, give skills assessments and offer career guidance.

Whats more, some companies are offering day in the life virtual simulations that allow candidates to see what a role would entail, which can either enhance interest or help candidates self-select out of the process. It also helps employers understand if the candidate would be a good fit, based on their behavior during the simulation. In Korn Ferrys global survey of HR professionals, 78 percent say that in the coming year, it will be vital to provide candidates with these day in the life type experiences.

Byrne Mulrooney, Chief Executive Officer, Korn Ferry RPO, Professional Search and Korn Ferry Digital

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Despite fears that it will replace human employees, in 2020 AI and machine learning will increasingly be used to aid and augment them. For instance, customer service workers need to be certain they are giving customers the right advice. AI can analyse complex customer queries with high numbers of variables, then present solutions to the employee speeding up the process and increasing employee confidence.

Lufthansa for one is already using this method, and with a faster, more accurate and ultimately more satisfying customer experience acting as a significant differentiator more will follow. Over the next three years this trend will keep accelerating, as businesses from banks to manufacturers use AI to support their employees decisions and outperform the competition.

Felix Gerdes, Director of Digital Innovation Services at Insight UK

In 2020 were going to see increased public demand for the demystification and democratisation of AI. There is a growing level of interest and people are quite rightly not happy to sit back and accept that a robot or programme makes the decisions it does because it does or that its simply too complicated. They want to understand how varying AI works in principle, they want to have more of a role in determining how AI should engage in their lives so that they dont feel powerless in the face of this new technology.

Companies need to be ready for this shift, and to welcome it. Increasing public understanding of AI, and actively seeking to hear peoples hopes and concerns is the only way forward to ensure that the role of AI is both seen as a force for good for everyone in our society and as a result able to realise the opportunity ahead historically not something that tech industry as a whole have been good at, we need to change.

Teg Dosanjh, Director of Connected Living for Samsung UK and Ireland

As the next decade of the transforming transportation industry unfolds, investment in autonomous vehicle development will continue to grow dramatically, especially in the datacenter and AI infrastructure for training and validation. Well see a significant ramp in autonomous driving pilot programs as part of this continued investment. Some of these will include removal of the on-board safety driver. Autonomous driving technology will be applied to a wider array of industries, such as trucking and delivery, moving goods instead of people.

Production vehicles will start to incorporate the hardware necessary for self-driving, such as centralized onboard AI compute and advanced sensor suites. These new features will help power Level 2+ AI assisted driving and lay the foundation for higher levels of autonomy. Regulatory agencies will also begin to leverage new technologies to evaluate autonomous driving capability, in particular, hardware-in-the-loop simulation for accurate and scalable validation. The progress in AV development underway now and for the next few years will be instrumental to the coming era of safer, more efficient transportation.

Danny Shapiro, Senior Director of Automotive, NVIDIA

As AI tools become easier to use, AI use cases proliferate, and AI projects are deployed, cross-functional teams are being pulled into AI projects. Data literacy will be required from employees outside traditional data teamsin fact, Gartner expects that 80% of organisations will start to roll out internal data literacy initiatives to upskill their workforce by 2020.

But training is an ongoing endeavor, and to succeed in implementing AI and ML, companies need to take a more holistic approach toward retraining their entire workforces. This may be the most difficult, but most rewarding, process for many organisations to undertake. The opportunity for teams to plug into a broader community on a regular basis to see a wide cross-section of successful AI implementations and solutions is also critical.

Retraining also means rethinking diversity. Reinforcing and expanding on how important diversity is to detecting fairness and bias issues, diversity becomes even more critical for organisations looking to successfully implement truly useful AI models and related technologies. As we expect most AI projects to augment human tasks, incorporating the human element in a broad, inclusive manner becomes a key factor for widespread acceptance and success.

Roger Magoulas, VP of Radar at OReilly

The hottest trend in the industry right now is in Natural Language Processing (NLP). Over the past year, a new method called BERT (Bidirectional Encoder Representations from Transformers) has been developed for designing neural networks that work with text. Now, we suddenly have models that will understand the semantic meaning of whats in text, going beyond the basics. This creates a lot more opportunity for deep learning to be used more widely.

Almost every organisation has a need to read and understand text and spoken word whether it is dealing with customer enquiries in the contact centre, assessing social media sentiment in the marketing department or even deciphering legal contracts or invoices. Having a model that can learn from examples and build out its vocabulary to include local colloquialisms and turns of phrase is extremely useful to a much wider range of organisations than image processing alone.

Bjrn Brinne, Chief AI Officer at Peltarion

Voice assistants have established themselves as common place in our personal lives. But 2020 will see an increasing amount of businesses turning to them to improve and personalise the customer experience.

This is because, advances in AI-driven technology and natural language processing are enabling voice interactions to be translated into data. This data can be structured so that conversations can be analysed for insights.

Next year, organisations will likely begin to embrace conversational analytics to improve their chatbots and voice applications. This will ultimately result in better data-driven decisions and improved business performance.

Alberto Pan, Chief Technical Officer, Denodo

Organisations are already drowning in data, but the flood gates are about to open even wider. IDC predicts that the worlds data will grow to 175 zettabytes over the next five years. With this explosive growth comes increased complexity, making data harder than ever to manage. For many organisations already struggling, the pressure is on.

Yet the market will adjust. Over the next few years, organisations will exploit machine learning and greater automation to tackle the data deluge.

Machine learning applications are constantly improving when it comes to making predictions and taking actions based on historical trends and patterns. With its number-crunching capabilities, machine learning is the perfect solution for data management. Well soon see it accurately predicting outages and, with time, it will be able to automate the resolution of capacity challenges. It could do this, for example, by automatically purchasing cloud storage or re-allocating volumes when it detects a workload nearing capacity.

At the same time, with recent advances in technology we should also expect to see data becoming more intelligent, self-managing and self-protecting. Well see a new kind of automation where data is hardwired with a type of digital DNA. This data DNA will not only identify the data but will also program it with instructions and policies.

Adding intelligence to data will allow it to understand where it can reside, who can access it, what actions are compliant and even when to delete itself. These processes can then be carried out independently, with data acting like living cells in a human body, carrying out their hardcoded instructions for the good of the business.

However, with IT increasingly able to manage itself, and data management complexities resolved, what is left for the data leaders of the business? Theyll be freed from the low-value, repetitive tasks of data management and will have more time for decision-making and innovation. In this respect AI will become an invaluable tool, flagging issues experts may not have considered and giving them options, unmatched visibility and insight into their operations.

Jasmit Sagoo, Senior Director, Head of Technology UK&I at Veritas Technologies

2020 will be the year research & investment in ethics and bias in AI significantly increases. Today, business insights in enterprises are generated by AI and machine learning algorithms. However, due to these algorithms being built using models and data bases, bias can creep in from those that train the AI. This results in gender or racial bias be it for mortgage applications or forecasting health problems. With increased awareness of bias in data, business leaders will demand to know how AI reaches the recommendations it does to avoid making biased decisions as a business in the future.

Ashvin Kamaraju, CTO for Cloud Protection and Licensing activity atThales

2020 will be the year of health data. Everyone is agreed that smarter use of health data is essential to providing better patient care meaning treatment that is more targeted or is more cost effective. However, navigating through the thicket of consents and rules as well as the ethical considerations has caused a delay to advancement of the use of patient data.

There are now several different directions of travel emerging which all present exciting opportunities for patients, for health providers including the NHS, for Digital Health companies and for pharmaceutical companies.

Marcus Vass, Partner, Osborne Clarke

Artificial intelligence isnt just something debated by techies or sci-fi writers anymore its increasingly creeping into our collective cultural consciousness. But theres a lot of emphasis on the negative. While those big picture questions around ethics cannot and should not be ignored, in the near-term we wont be dealing with the super-AI you see in the movies.

Im excited by the possibilities well see AI open up in the next couple of years and the societal challenges it will inevitably help us to overcome. And its happening already. One of the main applications for AI right now is driving operational efficiencies and that may not sound very exciting, but its actually where the technology can have the biggest impact. If we can use AI to synchronise traffic lights to impact traffic flow and reduce the amount of time cars spend idling, that doesnt just make inner city travel less of a headache for drivers it can have a tangible impact on emissions. Thats just one example. In the next few years, well see AI applied in new, creative ways to solve the biggest problems were facing as a species right now from climate change to mass urbanisation.

Dr Anya Rumyantseva, Data Scientist at Hitachi Vantara

Businesses are investing more in AI each year, as they look to use the technology to personalize customer experiences, reduce human bias and automate tasks. Yet for most organizations AI hasnt yet reached its full potential, as data is locked up in siloed systems and applications.

In 2020, well see organizations unlock their data using APIs, enabling them to uncover greater insights and deliver more business value. If AI is the brain, APIs and integration are the nervous system that help AI really create value in a complex, real-time context.

Ian Fairclough, VP of Services, MuleSoft

2020 is going to be a tipping point, when algorithmic decision making AI will become more mainstream. This brings both opportunities and challenges, particularly around the explainability of AI. We currently have many blackbox models where we dont know how its coming to decisions. Bad guys can leverage this and manipulate these decisions.

Using machine identities, they will be able to infiltrate the data streams that feed into an AI models and manipulate them. If companies are unable to explain and see the decision making behind their AI this could go unquestioned, changing the outcomes. This could have wide reaching impacts in everything from predictive policing to financial forecasting and market decision making.

Kevin Bocek, Vice President, Security Strategy & Threat Intelligence at Venafi

Until now, robotic process automation (RPA) and artificial intelligence (AI) have been perceived as two separate things: RPA being task oriented, without intelligence built in. However, as we move into 2020, AI and machine learning (ML) will become an intrinsic part of RPA infused throughout analytics, process mining and discovery. AI will offer various functions like natural language processing (NLP) and language skills, and RPA platforms will need to be ready to accept those AI skill sets. More broadly, there will be greater adoption of RPA across industries to increase productivity and lower operating costs. Today we have over 1.7 million bots in operation with customers around the world and this number is growing rapidly. Consequently, training in all business functions will need to evolve, so that employees know how to use automation processes and understand how to leverage RPA, to focus on the more creative aspects of their job.

RPA is set to see adoption in all industries very quickly, across all job roles, from developers and business analysts, to programme and project managers, and across all verticals, including IT, BPO, HR, Education, Insurance and Banking. To facilitate continuous learning, companies must give employees the time and resources needed to upskill as job roles evolve, through methods such as micro-learning and just in time training. In the UK, companies are reporting that highly skilled AI professionals, currently, are hard to find and expensive to hire, driving up the cost of adoption and slowing technological advancement. Organisations that make a conscious decision to use automation in a way that enhances employees skills and complements their working style will significantly increase the performance benefit they see from augmentation.

James Dening, Vice President for Europe at Automation Anywhere

Read more: Artificial intelligence to create 133 million jobs globally: Report

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Innovations in Artificial Intelligence-, Cloud-, and IoT-based Security, 2019 Research Report – ResearchAndMarkets.com – Business Wire

December 22, 2019 | Author: admin

DUBLIN--(BUSINESS WIRE)--The "Innovations in Artificial Intelligence-, Cloud-, and IoT-based Security" report has been added to ResearchAndMarkets.com's offering.

This Cyber Security TechVision Opportunity Engine (TOE) provides a snapshot on emerging cyber security solutions powered by artificial intelligence, cloud, and IoT innovations that help companies protect from threats, data breaches, phishing, other advanced and targeted attacks. They also defend against and prevent modern attacks residing within cloud, endpoints, and various network layers.

Cyber Security TechVision Opportunity Engine's mission is to investigate new and emerging developments that aim to protect the network infrastructure and the resources operating in the network. The TOE offers strategic insights that would help identify new business opportunities and enhance technology portfolio decisions by assessing new developments and product launches in: anti-spam, anti-virus, phishing, identity management, disaster recovery, firewalls, virtual private networks, end-point security, content filtering,

Web application security, authentication and access control, intrusion prevention and detection systems, encryption algorithms, cryptographic techniques, and pattern recognition systems for network security.

Highlights of this service include technology roadmapping of network security technologies; IP portfolio analysis; information on funding and investment opportunities; evaluation of commercial opportunities from technology developments; technology assessment; analysis of technology accelerators and challenges and many more.

Key Topics Covered:

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/n3ivvh

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Hanukkah Reflections on My Year of Toying With Bitcoin – Coindesk

December 22, 2019 | Author: admin

Tis the season for introspection. And this year, my thoughts are on bitcoin.

As Jewish people around the world celebrate Hanukkah this week, inspired by the ancient miracle that a sacred flame lasted for eight days although the oil supply was dangerously low, Im pondering how the hell Ill keep experimenting with this technology without burning the metaphorical candle at both ends.

I spent 2019 trying a variety of products and services to test how easy it is to actually use cryptocurrency. I ran a Casa bitcoin-lightning node, used decentralized exchanges (DEXs), moved bitcoin from mobile apps to a hardware wallet (a Ledger) then transacted straight from the hardware wallet.

Beyond just running the node, I used the Casa device to send invoices for a small product (a poetry book) to learn more about the challenges independent merchants might face. Lastly, I set up a BTCPay store, which is the stage of this experiment Ill end the year on.

And after a year of educational tinkering what is my takeaway?

Its this: Theres no way this technology is ready for prime time.

The most common refrain used to deflect from the technologys obvious shortcomings is that its still too early to build for usability. Precisely because I agree it is still very early, here are some lessons Ive learned about money that fellow bitcoiners might want to keep in mind before evangelizing to the moon to the masses any time soon.

1. Bitcoins usability relies on social capital

Lets start with what it took to sell a few poetry books using a bitcoin node.

There was an issue with my router, which I am terrible at describing other than saying something had to be configured with a port of sorts although the general internet connection worked fine. I clicked all the buttons.

If I didnt have experienced engineers in my rolodex of sources, I wouldnt have gotten past that first hurdle. I called up two of the smartest engineers I know. We opened up the raw code and shared screens so they could see. They cursed vigorously, assuring me I was doing everything right according to the Casa tutorial blog. We updated the node with a wee bit of manual configuration and they graciously opened up a channel to me. (Lightning access doesnt automatically offer two-way liquidity.)

Seasoned bitcoiners are generally able to find workarounds to overcome technical challenges (hardware wallet malfunction or incompatibility, incorrect updates, etc.). To be fair, the devices listed above are those I got to work (mostly) on my own. (I tried a few others and failed, which I wont list because this isnt a grouchy Yelp review.)

However, most command-line-only mountain men with their own custom setups dont realize how fickle some products are at this stage. If you actually rely on bitcoin for business, many non-custodial products and services are so experimental that you need tech support to operate them reliably. And why go the service-provider route at all with bitcoin? Be your own bank, remember?

Venmo works great. So does Stripe. Bitcoin needs to offer something different. (It does, of course. It might allow you to choose who you trust and what you trust them with, like bouncing a message across a mesh network, but well get to that later.)

2. Failure to transact

Some people are surely using bitcoin today to improve their financial self-sovereignty. The idea that anyone could do so, however, is laughable.

Even when the wallet-node-DEX setup was working smoothly, just a few of my (relatively tech-savvy) customers could find their transaction data with blockchain explorers. Bitcoin is only transparent to people with the skills to read this data. Without that knowledge, there is no benefit added by the public ledger. (There may even be risks associated with this ledger.)

For the time being, Id recommend any non-developer consider private consulting with an engineer (or membership to a startup service like Casas) as part of a crypto products price. That means you better expect to earn a pretty penny from bitcoiners as consumers (or savings liquidators) to make this worth it.

The trouble is, bitcoiners rarely want to spend their crypto. Herein lies the essential dilemma of bitcoin: It cant be money without payments, it is seen as too valuable to spend (unless you are facing censorship) and official payment systems require compliance.

Some of the sharpest engineers I know in places like Iran still struggle to use bitcoin because there arent enough people to transact with. Skills alone dont solve their legal problems. They also need a robust network of parties, both at home and abroad, who arent subject to the same compliance risks and government woes that caused their isolation in the first place.

3. Money is never trustless

Sanctions aside, all of these experiments reminded me of what it was like to get money in India during demonetization. Much like some token traders these days, when I was backpacking across India in 2016 my daily commerce relied on arbitrage. In short, social networks still control liquidity, whether its bitcoin or paper rupees.

I was at a camel festival in Rajasthan the first time I heard about demonetization. Indian businesses wouldnt accept my bills any more.

Instead, travelers were told to exchange them for new bills at the sole local bank. But the government didnt print enough new bills to cover the limited allotment each person was allowed per day (under $70).

When security guards came out with sticks to close the empty bank, the sunburnt and thirsty crowd grew rowdy. One guard grabbed a protester next to me by the face and shoved him toward the ground.

So I turned to the black market. In the next town, a middle-aged man with a moustache and a mobile-phone shop had a secret stash of new rupees. He would swap dollars at a steep premium, despite the best efforts of a teenage boy from a nearby village who haggled in the local language on my behalf, for a small fee.

Sometimes even my dollars didnt entice the currency dealer. This is the same issue Iranian bitcoiners face. Your currency is only valuable to people who believe they can also spend it in turn.

Like some bitcoiners in Venezuela who use cryptocurrency to get dollars today, foreigners turned to arbitrage during demonetization in India. Some businesses in 2016 charged foreigners less in euros than in dollars (with new rupees getting the lowest price of all), so we swapped among ourselves and developed relationships with business owners who would extend us short-term credit. Some banks and ATMs only had cash once a week. Premiums might fluctuate based on rumors of cash shipments on the horizon.

I started obsessively asking people how they managed their finances. Demonetization broke down stigmas around such topics, especially considering I was a petite traveler who could hardly bench press a housecat. From Pushkar to Varanasi and down south past Mumbai, most Indians told me they pooled their families wealth together with a single elder at the helm.

This echoes what is going on today in Lebanon and Iraq. In fact, the Indian outlet Economic Times referred to bitcoin as the new hawala, an ancient brokerage system often used for remittances. This social system almost resembles a mesh network.

Even in 2019, family networks are still the most popular financial networks. My bitcoin experiments got easier when I started treating cryptocurrency like black market rupees. It wasnt about going trustless as much as it was compartmentalizing trust across a network of social ties.

Who could I trust to get me to the next step of my bitcoin experiment? Theyd probably be exposed to my security setup if they helped circumnavigate some technical problems, but not others. Where was there opportunity for safe arbitrage?

Book buyers often trusted me with personal information too, which I could have connected to their wallet addresses or online aliases if I was nefarious. How does one lawfully and privately get a book to a buyer living in rural Latin America? Can bitcoin really connect people to the global economy, including but not limited to digital products? If so, that process requires trust on both ends.

Conclusions, for now

None of this is to say bitcoin isnt a global currency. The technology is already being used in this way. Transacting with wallets, especially European book-buyers, was the easiest part of my experiments.

Can cryptocurrency be used in a self-sovereign way, with minimal personal risks, to connect people who dont already have access to safer, more robust financial products?

That I cant say yet. It may depend on who burns the midnight oil in these early days, before prime time hits. Perhaps bitcoiners will be able to overcome the social challenges of money: Compliance, access, liquidity, usability. By comparison, the technical shortcomings are almost trivial.

In 2020, hopefully more people will try to transact outside their established networks and see what challenges they face in deliberately applying trust, rather than eliminating it. Can we trust in the bitcoin network? This crazy idea should have failed long ago. And yet, for over a decade bitcoin has already proven to be the experiment that flickers but never goes dark, almost like a candle.

The leader in blockchain news, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies. CoinDesk is an independent operating subsidiary of Digital Currency Group, which invests in cryptocurrencies and blockchain startups.

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Bitcoin Developer Throws Light on BTC’s Upcoming Future – Bitcoinist

December 22, 2019 | Author: admin

Bitcoin (BTC) attracts the most attention with its price action. But the technological capabilities of the leading coin should not be underestimated.

In 2020, more than a decade after the Genesis block was created, the Bitcoin network will have changed in unexpected ways. Beyond highly active mining and new ASIC coming in, Lightning Network is one of the aspects where BTC transfers will see a significant change.

A new Twitter thread based on analysis by Bitcoin developer John Newbery, suggests the Lightning Network may be one of the most active aspects of BTCs evolution. The network has its critics and detractors, but improvements allow for fast, truly peer-to-peer payments.

New technologies are being developed to make the LN function smoother, allowing multi-path payments and boosting reserves to ensure liquidity.Multiple teams are working on LN improvements, with Blockstream developing c-lightning, a light-weight implementation of the protocol. Eclair by ACINQ is a Scala-implementation, and other versions include LND and Rust Lightning.

The Lightning Networks, so far considered experimental, will spread across the Bitcoin ecosystem, the analysis predicts. After Bitfinex, other exchanges may start offering Lightning deposits.

Well see more lightning wallets: a mix of non-custodial; self-custodied with outsourced routing; and fully-self-managed wallets. This is a brand new space and therell be lots of experimentation. Different teams will find different niches to fill, suggests Newbery.

The legal status of the LN is still uncertain, as node operators are simply willing peers that transfer previously loaded funds by running the client. However, 2020 will also arrive with additional scrutiny on BTC transactions.

The Bitcoin network will also develop scaling solutions through second-layer technologies, the analysis suggests.

Taken together with taproot and SIGHASH_NOINPUT, well get extremely rich and private off-chain contracts will be made possible, explained Newbery.

Some of those technologies will return Bitcoins network to its older peer-to-peer status. Communications and connections could start happening directly between wallets, taking some of the load off nodes. Currently, most BTC nodes depend on cloud services, or require significant technical investment, and are run by miners.

Not all the implementations and innovations will be ready in 2020, but at least the beginnings may be put in place, commented Newbery. Bitcoin and its related protocols remain open-source and are still a form of community effort.

Although BTC is not overloaded now, scaling efforts continue, and the LN may be one of the tools ideal for some types of transactions. Second-layer solutions mean the Bitcoin network may compete with some of the biggest protocols offering smart contracts and other forms of distributed computation.

What do you think about the future of Bitcoin technology? Share your thoughts in the comments section below!

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