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) …
Both approaches have their pros and cons. The blog post Machine Learning and Real-Time Analytics in Apache Kafka Applications and the Kafka Summit presentation Event-Driven Model Serving: Stream Processing vs. RPC with Kafka and TensorFlow discuss this in detail. There are more and more applications where the analytic model is directly embedded into the event streaming application, making it robust, decoupled, and optimized for performance and latency. The model can be loaded into the application when starting it up (e.g., using the TensorFlow Java API). Model management (including versioning) depends on your build pipeline and DevOps strategy. For example, new models can be embedded into a new Kubernetes pod which simply replaces the old pod. Another commonly used option is to send newly trained models (or just the updated weights or hyperparameters) as a Kafka message to a Kafka topic.
Voice technology in education is taking over the academic sphere for both developers and learners. Marissa, from Alexa Education sheds more light on how the phenomenon is revolutionizing experiences in the education sector. Marissa has gathered massive passion for voice technology in education during her early days when she was producing CD-ROM educational content for'edutainment'. Her interest grew as she migrated to Microsoft where she worked on several projects that impacted the education industry such as Xbox and Encarta. Now, during her tenure in Amazon's Alexa Education, Marissa explains how her team is developing a solid connection between institutions and their stakeholders by providing efficient ways in which learners access educational content powered by technology.
Microsoft launched on Tuesday, February 18, the first artificial intelligence laboratory at the Bucharest Academy of Economic Studies (ASE), one of the largest economic higher education institutes in Romania. In the new lab, which required an investment of EUR 50,000, the students can find more about machine learning, create and test AI algorithms, store and manage huge volumes of data, or develop applications and platforms themselves, local Republica.ro As of March 3, 11 cloud engineers from Microsoft Romania will hold courses aimed at helping students develop both technical and business innovation skills. The first course to be held in the new cloud lab will focus on the latest innovations in information technology using Azure, Microsoft's cloud computing platform. The next courses will focus on artificial intelligence, and the ASE students will be encouraged to develop their own projects.
Hive is a full-stack deep learning platform helping to bring companies into the AI era. We take complex visual challenges and build custom machine learning models to solve them. For AI to work, companies need large volumes of high quality training data. We generate this data through Hive Data, our proprietary data labeling platform with over 1,000,000 globally distributed workers, generating millions of high quality pieces of data per day. We then use this training data to build machine learning models for verticals such as Media, Autonomous Driving, Security, and Retail.
Sitting at a clogged intersection, have you ever questioned how long the traffic systems have been around for? Sure, you probably have. The "modern" three-signal traffic light system was created in 1920 by a Detroit police officer and it has pretty much remained the same since--bigger, of course, but still basically the same. Digesting that information probably makes you wonder. Especially since there is a good chance you are sitting in a car that can keep your speed constant, brake for you, change lanes for you, sense your blind spots, provide directions, let you know something is wrong with the engine and, someday soon, can even periodically take over some of the driving for you. We are constantly being told how close we are to the largest transformation in the auto industry in history, yet the traffic management industry is still stuck in 1920.
Meet Spell – data science and machine learning startup that raised 15M$ to bring AI and deep learning to the global workforce. Spell is an end-to-end data science and machine learning platform that provides the infrastructure for companies and developers to prepare, train, deploy, and manage the full life-cycle of Machine Learning and Deep Learning experiments. Moreover, Spell was developed to streamline the MLOps process. Founded in 2017 by former Facebook engineer Serkan Piantino and Trey Lawrence, the company was born out of a desire to empower and transform the global workforce by making deep learning and AI accessible to everyone. It is an ideal solution for those looking to run multiple experiments in parallel and get fast results without worrying about overhead or infrastructure management.
Teikametrics, a leading SaaS provider of AI-powered optimization for brands and sellers on Amazon and Walmart, today announced the completion of a $15 million strategic funding round backed by new and existing investors. The announcement follows Teikametrics' selection as one of Walmart's first exclusive advertising optimization partners, and the addition of Srinivas Guddanti, a 14-year senior Amazon veteran, as its Chief Product Officer. Jump Capital led the round and were joined by follow-on investments from Granite Point Capital, MIT Professor of Econometrics, Jerry Hausman, and the former Head of Growth at Facebook and Uber, Ed Baker. "We're thrilled to lead this new round of capital in Teikametrics," said Michael McMahon, founding partner of Jump Capital. "The Company has grown rapidly, and the success of its proprietary AI technology for Amazon is a strong proof point for a broader ecommerce platform opportunity. The partnership with Walmart is a landmark event and we are excited to fund the expansion of the Teikametrics platform across multiple ecommerce channels."
A lot of organizations seek to engage closely with ML developers, either to increasing product adoption or crowdsource innovation. But a lot of these efforts fall into the trap of "seen-it-done-it-all" trap, where organizations employ the same strategies to engage them which they have utilized for other developers. Machine Learning developers have unique needs from the ecosystem. They face challenges that developers from another stream are largely insulated from. Firstly, ML is a fast-changing domain.
Being a cybersecurity analyst at a large company today is a bit like looking for a needle in a haystack -- if that haystack were hurtling toward you at fiber optic speed. Every day, employees and customers generate loads of data that establish a normal set of behaviors. An attacker will also generate data while using any number of techniques to infiltrate the system; the goal is to find that "needle" and stop it before it does any damage. The data-heavy nature of that task lends itself well to the number-crunching prowess of machine learning, and an influx of AI-powered systems have indeed flooded the cybersecurity market over the years. But such systems can come with their own problems, namely a never-ending stream of false positives that can make them more of a time suck than a time saver for security analysts.
These days almost every journalism conference has at least one session on the role of Artificial Intelligence (AI) in modern journalism and, interesting, it is always been asked: "will AI replace journalists and writers?". Last week I had an opportunity to visit the technology center of America's top news agency in Washington. There were using many tools and techniques to generate quick, accurate and foolproof contents using Artificial Intelligence (AI). These tools had multiple layers of data-centric AI wrappers to ensure the filtration of Fake News. During my visit, I was able to produce 550 words article, based on a press release, with a single click and amazingly this article had many relevant references from the past.