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The Anatomy of AI: Understanding Data Processing Tasks


But as your data scientists and data engineers quickly realize, building a production AI system is a lot easier said than done, and there are many steps to master before you get that ML magic. At a high level, the anatomy of AI is fairly simple. You start with some data, train a machine learning model upon it, and then position the model to infer on real-world data. Unfortunately, as the old saying goes, the devil is in the details. And in the case of AI, there are a lot of small details you have to get right before you can claim victory.

New Artificial Intelligence Instrument: GPT 3 and Legal Evaluation


Undoubtedly, one of the artificial intelligence models that have left its mark on the last period is GPT-3, in other words, Generative Pre-trained Transformer, Productive Pre-Processed Transformer 3 model in Turkish. GPT-3 was developed by OpenAI which is called an artificial intelligence R&D company that includes computer experts and investors such as Elon Musk, CEO of companies such as SpaceX Tesla, Sam Altman, known for her initiatives Loopt, Y Combinator, and Ilya Sutskever, one of the inventors of software and networks such as AlexNet, AlphaGo, TensorFlow, carries out projects and R & D studies in many groundbreaking areas, especially artificial intelligence. GPT-3 is defined as an autoregression language model that uses the deep learning method to produce content similar to texts and graphics are written and created by humans. It is stated that the system that processes data with "1.5" billion parameters in its previous version, GPT-2, will perform analysis with 175 billion parameters in GPT-3, so it can produce very advanced content. However, it is also stated that artificial intelligence that can produce such high quality and qualified content has many risks and can cause many problems.

IPI launches IPI Cloud AI


IPI, the digital contact centre specialist, announced the launch of IPI Cloud AI, a SaaS-based portfolio of IPI's own self-service applications teamed with AI capability from the world's leading vendors. The applications are seamlessly integrated to customers' existing contact centre infrastructures – whether they are on premise or cloud based – providing next-generation AI capability, enabling customers to harness the power of cloud-based self-service functionality simply and cost-effectively, within their existing contact centre system. The initial solutions available include IPI's premier self-service apps: Send Me, which directs customers away from the contact centre to an alternative digital channel; Q4 Me, IPI's own patented end-to-end call-back application; Tell Me, IPI's speech interface for relaying information back to the customers; and ID Me, IPI's ID&V with voice biometrics solution. Alongside this are native integrations to Google Dialogflow CX, Amazon Lex and Microsoft Cognitive Services to support full NLP and intent capture – regardless of channel. "Our research and development team has spent a long time developing a best of breed solution that takes our robust self-service applications and fuses them with next generation AI capabilities from the world's leading vendors," said Steve Murray, Solutions Director at IPI. "We feel that IPI Cloud AI strikes the right balance between cutting edge capabilities and ease of adoption all within an unmatched commercial model. It really is a gamechanger for the market."

AI data processing at the edge reduces costs, data latency – Urgent Comms


A race is on to accelerate artificial intelligence (AI) at the edge of the network and reduce the need to transmit huge amounts of data to the cloud. The edge, or edge computing, brings data processing resources closer to the data and devices that need them, reducing data latency, which is important for many time-sensitive processes, such as video streaming or self-driving cars. Development of specialized silicon and enhanced machine learning (ML) models is expected to drive greater automation and autonomy at the edge for new offerings, from industrial robots to self-driving vehicles. Vast computer resources in centralized clouds and enterprise data centers are adept at processing large volumes of data to spot patterns and create machine learning training models that "teach" devices to infer what actions to take when they detect similar patterns. But when those models detect something out of the ordinary, they are forced to seek intervention from human operators or get revised models from data-crunching systems.

The case against investing in machine learning: Seven reasons not to and what to do instead


The word on the street is if you don't invest in ML as a company or become an ML specialist, the industry will leave you behind. The hype has caught on at all levels, catching everyone from undergrads to VCs. Words like "revolutionary," "innovative," "disruptive," and "lucrative" are frequently used to describe ML. Allow me to share some perspective from my experiences that will hopefully temper this enthusiasm, at least a tiny bit. This essay materialized from having the same conversation several times over with interlocutors who hope ML can unlock a bright future for them. I'm here to convince you that investing in an ML department or ML specialists might not be in your best interest. That is not always true, of course, so read this with a critical eye. The names invoke a sense of extraordinary success, and for a good reason. Yet, these companies dominated their industries before Andrew Ng's launched his first ML lectures on Coursera. The difference between "good enough" and "state-of-the-art" machine learning is significant in academic publications but not in the real world. About once or twice a year, something pops into my newsfeed, informing me that someone improved the top 1 ImageNet accuracy from 86 to 87 or so. Our community enshrines state-of-the-art with almost religious significance, so this score's systematic improvement creates an impression that our field is racing towards unlocking the singularity. No-one outside of academia cares if you can distinguish between a guitar and a ukulele 1% better. Sit back and think for a minute.

Salesforce-backed AI project SharkEye aims to protect beachgoers


Salesforce is backing an AI project called SharkEye which aims to save the lives of beachgoers from one of the sea's deadliest predators. Shark attacks are, fortunately, quite rare. However, they do happen and most cases are either fatal or cause life-changing injuries. Just last week, a fatal shark attack in Australia marked the eighth of the year--an almost 100-year record for the highest annual death toll. Once rare sightings in Southern California beaches are now becoming increasingly common as sharks are preferring the warmer waters close to shore.

Artificial Intelligence Stocks To Buy And Watch Amid Rising AI Competition


Artificial intelligence stocks are rarer than you might think. Many companies tout AI technology initiatives and machine learning. But there really are few -- if any -- public, pure-play artificial intelligence stocks. The "AI" stock ticker, though, has been claimed. Startup, which sells AI software for the enterprise market, filed on Nov. 13 for an initial public offering. Thomas Siebel, who started Siebel Systems and sold it to Oracle for nearly $6 billion in 2006, founded Redwood City, Calif.-based

Dissecting's secret sauce: less about AI, more about fixing Hadoop


U.S. patent number 10,824,634, awarded this month. The diagram shows what the company calls a system of integration. A dotted line represents an enclosing wrapper of types that can be referenced to simplify the development of applications that join together resources, such as a data integration unit and a machine learning unit and a MapReduce component., the software company founded by software industry legend Tom Siebel, which on Friday filed to go public, describes its purpose in life as applying artificial intelligence to sales and marketing. What it is actually doing appears to be much more fixing the sins of infrastructure software such as Hadoop, and its commercial implementations by Cloudera and others.

BraveIT Session: The Business Applications of AI


AI is useful in human resources, worker training, and professional development. One new AI-powered application, called Avenues, trains social workers through virtual reality scenarios using an Oculus VR headset, natural language processor, and a database of past child welfare cases. The social worker is confronted with various domestic situations and asked to decide on the best course of action, such as remove a child from the house or offer parents different support services. Created by Accenture, the Avenues application takes social workers through an immersive training experience that includes much of ambiguity and stress of real-life situations but allows them to practice making the tough decisions without real-life consequences.