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) …
Clustering is an important part of the machine learning pipeline for business or scientific enterprises utilizing data science. As the name suggests, it helps to identify congregations of closely related (by some measure of distance) data points in a blob of data, which, otherwise, would be difficult to make sense of. However, mostly, the process of clustering falls under the realm of unsupervised machine learning. And unsupervised ML is a messy business. There is no known answers or labels to guide the optimization process or measure our success against.
Domain Generation Algorithms (DGAs) are frequently used to generate large numbers of domains for use by botnets. These domains are often used as rendezvous points for the servers that malware has command and control over. There are many algorithms that are used to generate domains, but many of these algorithms are simplistic and are very easy to detect using classical machine learning techniques. In this paper, three different variants of generative adversarial networks (GANs) are used to improve domain generation by making the domains more difficult for machine learning algorithms to detect. The domains generated by traditional DGAs and the GAN based DGA are then compared by using state of the art machine learning based DGA classifiers.
This is the original Tensorflow implementation of the ACL 2019 paper Simple and Effective Text Matching with Richer Alignment Features. RE2 is a fast and strong neural architecture for general purpose text matching applications. In a text matching task, a model takes two text sequences as input and predicts their relationship. This method aims to explore what is sufficient for strong performance in these tasks. It simplifies or omits many slow components which are previously considered as core building blocks in text matching.
Get your data questions answered by a leading expert. Executives who rely on data analytics face a squeeze from two directions. On one hand, analytics continues to be ever more important in making business decisions – if you're not getting the most from your analytics solution, you're likely falling behind. Yet on the other hand, data analytics continually grows more complex, as advances in software and methodology enables greater insight, but also greater operational challenge. To shed light on the rapidly growing data analytics sector, I'll speak with two leading experts: Andi Mann, Chief Technology Advocate at Splunk, and Bill Schmarzo, CTO, IoT and Analytics, Hitachi Vantara.
With a new $1.5 million grant, the growing field of transfer learning has come to the Ming Hsieh Department of Electrical and Computer Engineering at the USC Viterbi School of Engineering. The grant was awarded to three professors -- Salman Avestimehr, Antonio Ortega and Mahdi Soltanolkotabi -- who will work with Ilias Diakonikolas at the University of Wisconsin, Madison, to address the theoretical foundations of this field. Modern machine learning models are breaking new ground in data science, achieving unprecedented performance on tasks like classifying images in one thousand different image categories. This is achieved by training gigantic neural networks. "Neural networks work really well because they can be trained on huge amounts of pre-existing data that has previously been tagged and collected," said Avestimehr, the primary investigator of the project.
Tata Consultancy Services' New Unit will Help Enterprises Leverage the Power of AI, Machine Learning and Cloud to Pursue their Growth and Transformation Agenda using Microsoft technologies REDMOND MUMBAI, November 18, 2019: Tata Consultancy Services (TCS) (BSE: 532540, NSE: TCS), a leading global IT services, consulting and business solutions organization, announced the setting up of a new Microsoft Business Unit (MBU). Offering a full complement of services and solutions around Microsoft technologies, and catering to all stakeholders in the enterprise, the new unit will work with customers worldwide to accelerate their Business 4.0 transformation journeys. The new unit will leverage TCS' deep domain knowledge across industries and global talent pool of nearly 50,000 engineers trained on Microsoft technologies, to help customers leverage the power of AI, automation and cloud to enhance customer experience, re-imagine employee empowerment, optimize operations and spur innovation. Moreover, TCS' unique Location Independent Agile model will help customers accelerate their transformation journeys and achieve superior outcomes with unmatched speed to value. For customers looking to scale up their innovation efforts, the new unit will provide a ready means of plugging into TCS' extensive co-innovation ecosystem and pushing the boundaries of possibilities using the full stack of Microsoft technologies to establish competitive differentiation.
Businesses and investors universally believe Artificial Intelligence (AI) and Machine Learning (ML) for can fuel new revenue and profit growth by reinventing customer journeys, transforming the customer experience, and optimizing investments in marketing and channels. Consequently, Investment in AI and ML for marketing applications is booming. Despite this frenzy of spending, the impact these investments on growth is not very clear. Most marketers still do not understand how data and analytics will contribute to profit growth according to the Forbes Marketing Accountability Report. And the majority of CFOs struggle to prove the financial return on their investment in analytics.
We've got lots of data in Loss Prevention (LP): months of video stored somewhere in our infrastructure and multiple systems that house financial data. For years, we've described the need for a data pipeline to consolidate all that data into one place to better understand our business. The data pipeline implementation takes the raw data in an operation and stores it in a data lake. From the data lake, one can join all this disparate data and video together in multiple ways to derive more insight from these assets. Let's say an LP staff member finds a new exception scenario that is causing your organization to lose money.
As new technologies automate more traditional and routine tasks, executives and employees recognize that emotional intelligence (EI) skills – such as self-awareness, self-management, social awareness and relationship management – will be a key requisite for success in the years to come. While demand for EI skills is set to increase by six times in the next 3-5 years, recruitment and training in this area has mostly failed to adapt. This is set to leave many companies unable to reap the benefits EI offers in terms of employee satisfaction, revenue generation, lower attrition and cost reductions. The "Emotional intelligence – the essential skillset for the age of AI" report from the Capgemini Research Institute provides a global look at how companies view EI and recommends that they combine technology with the talent to develop relevant skills among their employees. Executives said employees need to develop EI skills so they can adapt to more client/person-facing roles (76%) and take on tasks requiring EI skills that cannot be automated (also 76%) such as empathy, influence and teamwork.
This paper conducts a comparative study of various learning models on Hate and Abusive Speech on Twitter dataset. The following script will install required Python packages. We do not provide the code for crawling text data. While creating the pickle file, you will need to create a python dictionary with the following structure: _dict[_id] [tweet_label, tweet_text, context_text]. If your dataset does not have context, fill '' as the third element of each instance.)