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) …
As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning). Supervised Learning relies on a process of labeling in order to "understand" information.
The News: Last week, Apple's acquisition of Xnor.ai was reported, no doubt aiming to deliver TinyML to edge devices. Xnor.ai, a Seattle startup specializing in low-power, edge-based artificial intelligence (AI) tools. Spun off from the Allen Institute for Artificial Intelligence, the three-year-old startup's technology embeds AI on the edge, enabling facial recognition, natural language processing, augmented reality, and other ML-driven capabilities to be executed on low-power devices rather than relying on the cloud. Analyst Take: Developers of AI applications for edge deployment are doing their work in a growing range of frameworks and deploying their models to myriad hardware, software, and cloud environments. This complicates the task of making sure that each new AI model is optimized for fast inferencing on its target platform, a burden that has traditionally required manual tuning.
The global movie industry generated over $43 billion in revenue in 2018, of which the United States' contribution alone topped more than $11 billion. Yet, these seemingly impressive headline figures can obscure the fact that year-on-year growth has been a sluggish 2 per cent over the last several years, with market researchers forecasting further stagnation. Given the inherent financial risk involved in film making, some now believe artificial intelligence, rather than human expertise, is best placed to select which films are most likely to provide suitable returns on investment. In early January 2020, Warner Bros signed a deal with Cinelytic, a Los Angeles-based artificial intelligence company which, according to the press release, aims to help content creators make faster, better-informed decisions through predictive analytics. Belgium's ScriptBook provides a similar service, touted as "artificially intelligent script analysis and box office forecasting".
The secret in much of artificial intelligence today is that it depends on hordes of unskilled workers to label the data used to train supervised learning models. But, in order for data science teams to work with labelers around the world, they need a platform. This week, in the second of a periodic series of sponsored episodes, I talk to Manu Sharma and Brian Rieger, who saw the opportunity to provide that platform and founded Labelbox, the leading labelling software in the space.
Today's Daily AI Roundup covers the latest Artificial Intelligence announcements on AI capabilities, AI mobility products, Robotic Service, Technology from Bright Pattern, Microsoft Corp. Genesys, Grammarly Business, Metadata.io. Bright Pattern, the most powerful cloud contact center with AI for innovative companies, announced the release of Bright Pattern Omnichannel Quality Management (Omni QM). Omni QM is embedded in the Bright Pattern Omnichannel platform, and allows quality management on all channels in a single interface. Microsoft Corp. and Genesys have expanded their partnership to provide enterprises with a new cloud service for contact centers that enables them to deliver superior interactions for customers. With the omnichannel customer experience solution Genesys Engage running on Microsoft Azure, enterprises have the security and scalability they need to manage the complexities involved with connecting every touchpoint throughout the customer journey.
Workflow-integrated storage supplier Iguazio has received $24m in C-round funding and announced its Data Science Platform. This is deeply integrated into AI and machine learning processes, and accelerates them to real-time speeds through parallel access to multi-protocol views of a single storage silo using data container tech. The firm said digital payment platform provider Payoneer is using it for proactive fraud prevention with real-time machine learning and predictive analytics. Yaron Weiss, VP Corporate Security and Global IT Operations (CISO) at Payoneer, said of Iguazio's Data Science Platform: "We've tackled one of our most elusive challenges with real-time predictive models, making fraud attacks almost impossible on Payoneer." He said Payoneer had built a system which adapts to new threats and enables is to prevent fraud with minimum false positives.
Online learning (aka E-Learning) is now considered to be an integral part of the education sector. In simple words, online learning refers to the type of learning where the learning process is mediated by the internet i.e. the learners use the internet to learn. Online learning is gaining tremendous popularity. It is also said to increase the knowledge retention rates from 25-60% in comparison to face-to-face training. Online learning owes much of its popularity and efficiency to machine learning (ML) and artificial intelligence (AI).
Are you keeping up with internal communication trends? Of course the basics of communications never changes, we seek to gain the attention of an audience with a relevant message they will engage with. However, just like any other industry, the technology and channels of communications changes over time, and it's wise to stay current. It is important to note that trendy doesn't equal effective. This means that you need to stay up to date on what works and what doesn't to maximize effective communication within your company.
"There's a lot of value in the data that organizations collect, and HPC and AI are helping organizations get the most out of this data," said Thierry Pellegrino, vice president of HPC at Dell Technologies. "We're committed to building solutions that simplify the use and deployment of these technologies for organizations of all sizes and at all stages of deployment." Dell is expanding its portfolio of Dell EMC Ready Solutions for HPC Storage with new, turnkey solutions for ThinkParQ's BeeGFS and ArcaStream's PixStor file systems. Offering a combination of technology partners' software with Dell EMC hardware, networking and support, based on engineered and tested designs, Dell EMC Ready Solutions for HPC Storage simplify and speed deployment and solutions management. Dell EMC Ready Solutions for HPC BeeGFS Storage, with ThinkParQ's software-defined parallel file system, speeds-up input/output (I/O)-intensive workloads with the ability to scale from small clusters to enterprise-class systems on premises or in the cloud.
AI appears set to be the thing that separates the next generation of business success stories and market dropouts. It has revolutionized the transportation industry by bringing the science fiction dream of autonomous cars into reality, as driverless taxis have already been tested and deployed in the U.S. Further indicators of its importance come from finance companies like Goldman Sachs, JPMorgan and Morgan Stanley -- all of which have aggressively expanded their data and tech teams over the past year -- looking to deploy AI projects that will give them the competitive edge against their rivals. The application of this technology ranges from the mundane to the absurd, seemingly with no sector able to escape its influence -- and the pharma industry is no different. From personal experience, having spent nearly three decades working in the technology industry, and from many conversations with my better half, a longtime pharmaceutical research professional in the therapeutics and drug-discovery sector, there's no question the opportunity for AI in pharma is immense. Some of the industry's giants have already started to take the plunge and implement AI strategies for an array of different purposes, setting the stage for industry transformation.