Category Archives: Artificial Intelligence
What Is The Artificial Intelligence Of Things? When AI Meets IoT – Forbes
Individually, the Internet of Things (IoT) and Artificial Intelligence (AI) are powerful technologies. When you combine AI and IoT, you get AIoTthe artificial intelligence of things. You can think of internet of things devices as the digital nervous system while artificial intelligence is the brain of a system.
What Is The Artificial Intelligence Of Things? When AI Meets IoT
What is AIoT?
To fully understand AIoT, you must start with the internet of things. When things such as wearable devices, refrigerators, digital assistants, sensors and other equipment are connected to the internet, can be recognized by other devices and collect and process data, you have the internet of things. Artificial intelligence is when a system can complete a set of tasks or learn from data in a way that seems intelligent. Therefore, when artificial intelligence is added to the internet of things it means that those devices can analyze data and make decisions and act on that data without involvement by humans.
These are "smart" devices, and they help drive efficiency and effectiveness. The intelligence of AIoT enables data analytics that is then used to optimize a system and generate higher performance and business insights and create data that helps to make better decisions and that the system can learn from.
Practical Examples of AIoT
The combo of internet of things and smart systems makes AIoT a powerful and important tool for many applications. Here are a few:
Smart Retail
In a smart retail environment, a camera system equipped with computer vision capabilities can use facial recognition to identify customers when they walk through the door. The system gathers intel about customers, including their gender, product preferences, traffic flow and more, analyzes the data to accurately predict consumer behavior and then uses that information to make decisions about store operations from marketing to product placement and other decisions. For example, if the system detects that the majority of customers walking into the store are Millennials, it can push out product advertisements or in-store specials that appeal to that demographic, therefore driving up sales. Smart cameras could identify shoppers and allow them to skip the checkout like what happens in the Amazon Go store.
Drone Traffic Monitoring
In a smart city, there are several practical uses of AIoT, including traffic monitoring by drones. If traffic can be monitored in real-time and adjustments to the traffic flow can be made, congestion can be reduced. When drones are deployed to monitor a large area, they can transmit traffic data, and then AI can analyze the data and make decisions about how to best alleviate traffic congestion with adjustments to speed limits and timing of traffic lights without human involvement.
The ET City Brain, a product of Alibaba Cloud, optimizes the use of urban resources by using AIoT. This system can detect accidents, illegal parking, and can change traffic lights to help ambulances get to patients who need assistance faster.
Office Buildings
Another area where artificial intelligence and the internet of things intersect is in smart office buildings. Some companies choose to install a network of smart environmental sensors in their office building. These sensors can detect what personnel are present and adjust temperatures and lighting accordingly to improve energy efficiency. In another use case, a smart building can control building access through facial recognition technology. The combination of connected cameras and artificial intelligence that can compare images taken in real-time against a database to determine who should be granted access to a building is AIoT at work. In a similar way, employees wouldn't need to clock in, or attendance for mandatory meetings wouldn't have to be completed, since the AIoT system takes care of it.
Fleet Management and Autonomous Vehicles
AIoT is used to in fleet management today to help monitor a fleet's vehicles, reduce fuel costs, track vehicle maintenance, and to identify unsafe driver behavior. Through IoT devices such as GPS and other sensors and an artificial intelligence system, companies are able to manage their fleet better thanks to AIoT.
Another way AIoT is used today is with autonomous vehicles such as Tesla's autopilot systems that use radars, sonars, GPS, and cameras to gather data about driving conditions and then an AI system to make decisions about the data the internet of things devices are gathering.
Autonomous Delivery Robots
Similar to how AIoT is used with autonomous vehicles, autonomous delivery robots are another example of AIoT in action. Robots have sensors that gather information about the environment the robot is traversing and then make moment-to-moment decisions about how to respond through its onboard AI platform.
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What Is The Artificial Intelligence Of Things? When AI Meets IoT - Forbes
Deciphering Artificial Intelligence in the Future of Information Security – AiThority
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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Deciphering Artificial Intelligence in the Future of Information Security - AiThority
7 tips to get your resume past the robots reading it – CNBC
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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The Machines Are Learning, and So Are the Students – The New York Times
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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The Machines Are Learning, and So Are the Students - The New York Times
Tommie Experts: Ethically Educating on Artificial Intelligence at St. Thomas – University of St. Thomas Newsroom
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
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.
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Zebra Medical Vision Announces Agreement With DePuy Synthes to Deploy Cloud Based Artificial Intelligence Orthopaedic Surgical Planning Tools -…
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
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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Artificial intelligence predictions for 2020: 16 experts have their say - Verdict
Innovations in Artificial Intelligence-, Cloud-, and IoT-based Security, 2019 Research Report – ResearchAndMarkets.com – Business Wire
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.
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Finland offers crash course in artificial intelligence to EU – The Associated Press
HELSINKI (AP) Finland is offering a techy Christmas gift to all European Union citizens a free-of-charge online course in artificial intelligence in their own language, officials said Tuesday.
The tech-savvy Nordic nation, headed by the worlds youngest head of government, is marking the end of its rotating presidency of the EU with a highly ambitious goal.
Instead of handing out the usual ties and scarves to EU officials and journalists, the Finnish government led by the 34-year-old Prime Minister Sanna Marin has opted to present a less conventional gift not only to a selected few but all EU citizens.
Finland, a nation of 5.5 million that will hand over the EU presidency to Croatia at the end of the year, is aiming to give practical understanding of AI to 1% of EU citizens or about 5 million people through a basic online course by the end of 2021.
It is teaming up with the University of Helsinki, Finlands largest and oldest academic institution, and the Finland-based tech consultancy Reaktor.
Teemu Roos, a University of Helsinki associate professor in the department of computer science, described the nearly $2 million project as Finlands gift to Europe and a civics course in AI for every EU citizen to cope with the societys ever-increasing digitalization and the possibilities AI offers to the job market and elsewhere.
The course covers elementary AI concepts in a practical way and doesnt go into deeper concepts like coding, he said.
We have enormous potential in Europe but what we lack is investments into AI, Roos said, adding that the continent faces fierce AI competition from digital giants like China and the United States.
Timo Harakka, the Finnish minister for transport and communications, said last week that the country wants to equip EU citizens with digital skills for the future and .... to give a boost to the digital leadership of Europe through the project.
The initiative is for paid by the Finnish ministry for economic affairs and employment, and officials said the course is meant for all EU citizens whatever their age, education or profession.
Since its launch in Finland in 2018 The Elements of AI has been phenomenally successful the most popular course ever offered by University of Helsinki, which traces its roots back to 1640 with more than 220,000 students from over 110 countries having taken it so far online, Roos said.
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Finland offers crash course in artificial intelligence to EU - The Associated Press