If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In recent years, many new clinical diagnostic tools have been developed using complicated machine learning methods. Irrespective of how a diagnostic tool is derived, it must be evaluated using a 3-step process of deriving, validating, and establishing the clinical effectiveness of the tool. Machine learning–based tools should also be assessed for the type of machine learning model used and its appropriateness for the input data type and data set size. Machine learning models also generally have additional prespecified settings called hyperparameters, which must be tuned on a data set independent of the validation set. On the validation set, the outcome against which the model is evaluated is termed the reference standard.
Community Data Coop presents its continuing series of Education Spotlight events! Our next event is a panel discussion on Artificial Intelligence and Ethics. The proliferation of AI in every aspect of our lives has resulted in many new and exciting advances, but the speed at which the field has developed has resulted in many concerns from privacy to labor replacement. Come hear from a panel of AI professionals as they discuss the true impact of Artificial Intelligence on our lives beyond the obvious pros and cons. Alissa Yeganeh, Senior Consultant, Coppei Partners - Alissa Yeganeh is a Data-Focused Customer & Operations leader, currently working as a Senior Consultant at Coppei Partners, a business and technology consulting firm based in Mercer Island, WA.
When most people think of artificial intelligence, that scene with HAL-9000's glowing red eye from Stanley Kubrick's 1968 film "2001: A Space Odyssey" probably comes to mind. Even though that particular AI convinced millions of moviegoers that artificial intelligence was evil by refusing to open the pod bay doors, real AI is actually all around us, improving our quality of life and simplifying the way businesses function.
Leading experts in cybersecurity and ethics from Oxford Internet Institute, University of Oxford, Dr Mariarosaria Taddeo and Professor Luciano Floridi, and Professor Tom McCutcheon from Defence Science and Technology Laboratories believe the current approach to defining standards and certification procedures for Artificial Intelligence (AI) systems in cybersecurity is risky and should be replaced with an alternative method. Their new paper "Trusting Artificial Intelligence in Cybersecurity: a Double-Edged Sword", published in the journal Nature Machine Intelligence argues that defining standards based on placing implicit trust in AI systems to perform as expected, without any degree of any monitoring or control, could leave us at risk of new forms of AI attacks, disrupting systems and changing their behaviour. Current'trust' based standards and certification procedures in AI typically see tasks being carried out with either no or minimal control on the way the AI-driven tasks are performed. In their paper, the cybersecurity experts present the case for developing'reliable' rather than trustworthy AI in cybersecurity. The experts argue that reliable AI has greater potential to ensure the successful deployment of AI systems for cybersecurity tasks, making them less vulnerable to cyber-attacks.
Convolutional Neural Network (CNN) is a special type of deep neural network that performs impressively in computer vision problems such as image classification, object detection, etc. In this article, we are going to create an image classifier with Tensorflow by implementing a CNN to classify cats & dogs. With traditional programming is it not possible to build scalable solutions for problems like computer vision since it is not feasible to write an algorithm that is generalized enough to identify the nature of images. With machine learning, we can build an approximation that is sufficient enough for use-cases by training a model for given examples and predict for unseen data. CNN is constructed with multiple convolution layers, pooling layers, and dense layers.
NEC Corporation, a leading firm in network technologies and IT, and VAXIMM AG, a biotech firm focused on the development of oral T-cell immunotherapies, recently announced that both the companies inked a strategic agreement for a clinical trial partnership and an equity investment for the development of innovative personalized neoantigen vaccines for cancer. The terms of this non-exclusive partnership agreement, NEC will be providing funding for the Phase I clinical trial. VAXIMM and NEC will be co-developing personalized vaccines for cancer by using the advanced artificial intelligence (AI) technology of NEC which is used in both of its Neoantigen prediction System and propriety T-cell immunotherapy technology of VAXIMM. Sources informed that the vaccines will be evaluated for several solid tumors in Phase 1 clinical trial. VAXIMM is given the responsibility to conduct the clinical trial that is anticipated to start in 2020.
The accelerated pace of success in machine learning applications reflects significant improvements in specialized hardware, algorithms, and access to data. Corporations now know that they need to deploy machine learning rapidly, but they are unsure about best practices. Public sentiment has shifted from skepticism to fears of runaway AI, as well as employment, privacy, and ethical issues. The future success of machine learning depends on our ability to capture competitive advantages and manage downside risks effectively. About Singularity University: Singularity University is a benefit corporation headquartered at NASA's research campus in Silicon Valley.
Four years ago, I stood in the darkened operations center in front of a wall of blinking screens, arms crossed and squinting at video footage on one of them. The commander asked me for the second time, signaling toward the figure on the screen. I looked over and reviewed a mental checklist of the individual's pattern of life over more than a decade. I weighed this against his latest movements, reflected on the screen in real time. The commander took a step toward me and started again, "Kara. We are running out of time. I had a decision to make. Using a machine to determine the validity of the target and take action is a nonstarter. But not everyone agrees on the details. Though the machines I dealt with that day were only semi-autonomous, it is not difficult to imagine a world where fully autonomous weapons are programmed to make a lethal decision. Institutions, countries, industry, and society must choose when and how to govern this technology in today's world, where semi-autonomous ...