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) …
Two words that seem to be at odds with each other, especially if you look at them within the context of phone channels in customer relations! It's somewhat counterintuitive, particularly when you factor in the roles they play in brand's strategies regarding customer relations optimization. To begin with, let's define what these two terms mean in the field of customer relations: What's the new, shared ambition bringing these two extremes together? Indeed, properly automated, tailor-made customer journeys via phone channels will no longer be viewed, wrongly, as a necessary evil by some customers, nor considered a costly proposition by most brands. They will be perceived as an integral element praised both for its efficiency and its personal touch.
Since the dawn of chatbots and digital assistant creation, one critique has been universal: the helper is not human-like enough. This issue spans enterprises, and IT developers and startups are now developing AI that is human-like, emotional and responsive. Christian Selchau-Hansen, CEO of enterprise software company Formation and former manager of product at social game development company Zynga, said that one of the major uses of AI in video games is the implementation of generative adversarial network (GAN) technology, image recognition and replication in character design. The ability of an algorithm to read emotion, generate emotion from text and accurately portray emotion enables a heightened level of gameplay. "Whether it's GPT-3 or the processing and techniques of developments like deepfakes … the good things that come from [these developments] are more immersive worlds," Selchau-Hansen said.
In late 2016, Adobe announced that Sensei, the company's AI technology, would begin to power and assist in some of its Digital Media applications, such as Photoshop and Illustrator. While that was only 3 short years ago, in the dawning of the AI era, Sensei's role makes Adobe one of the pioneers of machine learning- and deep learning-powered AI. What started in 2016 as narrow AI technology aimed at narrow use cases has become an AI engine that, according to Tatiana Mejia, group Product Marketing manager for Sensei, now powers dozens of different features across Adobe. "We don't tag any of the features with Sensei, it's just the engine behind Adobe products," said Mejia. Whether the technology is effective or not, the concept is advanced AI thinking.
As expected, artificial intelligence (AI) and machine learning (ML) applications are already having an impact on society. Many industries that we tap into daily--such as banking, financial services and insurance (BFSI), and digitized health care--can benefit from AI and ML applications to help them optimize mission-critical operations and execute functions in real time. The BFSI sector is an early adopter of AI and ML capabilities. Natural language processing (NLP) is being implemented for personal identifiable information (PII) privacy compliance, chatbots and sentiment analysis; for example, mining social media data for underwriting and credit scoring, as well as investment research. Predictive analytics assess which assets will yield the highest returns.
In production, waste comes in many shapes and sizes. For some businesses, it could be'reject' products that don't fit the desired weight, size, or shape. For others, it could be a question of quality – such as colour or moisture variances – or raw materials variability. Whatever the form of the waste, production losses are'very important challenges to deal with', according to Seebo co-founder and CEO Lior Akavia. The start-up was born out of a desire to cut waste in production lines, both in prediction and prevention.
Juniper Networks has further expanded several AI solutions within its Mist portfolio. The improvements, such as the Mist WAN Assurance tool and an update to the Marvis AI engine, will make it easier for businesses to automatically detect network problems and resolve connectivity issues. Juniper Networks is strongly committing to AI for detecting problems within corporate networks and the underlying connections. The portfolio of Mist, which was acquired last year, has played a key role. With the tools of Mist, companies are able to automatically detect and solve network problems in (wireless) networks.
Juniper Networks today announced it can now apply the same artificial intelligence (AI) engine it uses to automate the management of wireless local area networks (LANs) to software-defined wide area networks (SD-WANs). Based on an AI engine that Juniper Networks gained when it acquired Mist Systems, the Juniper Mist WAN Assurance cloud service streams telemetry data gathered from Juniper SRX devices to an AI engine that advises IT teams on how to best set WAN service levels as part of a larger transition to intent-based networking. As part of that initiative, Juniper Networks is also providing access to a Marvis conversational interface originally developed by Mist to enable IT teams to verbally issue commands, replacing the traditional command-line interface (CLI). Christian Gilby, director of product marketing for Juniper Networks, said as the number of remote branch offices that IT organizations need to manage increases at a time when travel is limited, the need to apply AI to SD-WANs has never been more apparent. Rather than return to central offices in the wake of the COVID-19 pandemic, Gilby noted, many organizations are shifting staff to smaller remote offices to enable collaboration while continuing to minimize potential exposure to anyone who might be infected.
Just in time to get you into the holiday spirit, we are now proud to release digiKam 7.0.0 This version is a result of a long development that started one year ago and in which we have introduced new features and plenty of fixes. Check out some of the highlights listed below and discover all the changes in detail. For many years, digiKam has provided an important feature dedicated to detecting and recognizing faces in photos. The algorithms used in the background (not based on deep learning) were old and had been unchanged since the first revision that included this feature (digiKam 2.0.0). It had the peoblem of not being powerful enough to facilitate the faces-management workflow automatically.
Police Officer aims his Lidar, towards drivers that may be speeding (PAUL J. RICHARDS/AFP via Getty ... [ ] Images) As LiDAR matures into a critical sensor for ADAS and Autonomous Vehicles (AVs), balancing the styling aspects of vehicle integration with functionality is becoming increasingly important. In this sense, LiDAR is simply following in the footsteps of legacy sensors like radar, cameras, and ultrasonic. They were highly visible in early deployments, but are generally invisible today. In the absence of this, components like the laser, detector, and scanner will heat up and cause performance and reliability issues. Solid-state LiDAR (either flash or using solid-state scanning) has clear advantages in this regard.