Library sales to streaming companies have helped to keep revenue flowing for content owners during lockdown. Now, they can take advantage of a new technology that can digitally insert branded products and promotional items into finished content. Its platform uses AI to identify the most natural and meaningful placement opportunities, and then employs VFX technology to insert real-world objects that weren't in the original shoot, like a vehicle or a bag of potato chips, or overlays existing brand imagery with new product shots. Mirriad can boast an impressive list of media clients it has worked with, including Tencent Video, 20th Century Fox, RTL, Channel 4, France TV and ABC. Brands they have helped include Pepsi, Sherwin Williams, P&G, Huawei and T-Mobile.
In enterprise ML architectures, it's wise to maintain the outputs of the feature jobs in a sharable format without encoding. These features can be later cherrypicked, encoded, and fed into an ML model that needs it. This approach has several advantages. When features are readily available, the journey from a'business question' to'scientific answer' becomes much more simple. With the availability of feature pool, when a data scientist wants to do a new experiment, he/she does not have to start from the raw data. Instead he/she can start with the available features. This can avoid a lot of unoptimised runs. In the cases where they need more data-features, it can go as a request to the engineering team to optimally build whatever new is requested. And when they are confident to take the model to production environment, the model promotion will involve only minimal components.
With e-commerce marketplaces saturated with high-end brands, cheaper variations of these products have become commonplace over time. Additionally, many consumers have consistently complained about receiving counterfeit products from their online purchases, leading to various policy actions. Fake products are becoming ubiquitous across categories – from electronics and furniture to apparels and cosmetics. But is there a way to detect counterfeit products before they reach a customer, or even listed on a website? A slew of initiatives like Amazon-led AntiCounterfeiting Coalition (IACC) and Alibaba Big Data Anti-Counterfeiting Alliance have come up in recent years to combat this problem.
It is our pleasure to welcome Alejandro Betancourt, Ph.D., General Manager and Sr. Machine learning tech lead at LANDING AI for TMC's next digital impact session on Tuesday, July 14 at 17:00 (CET). Recent breakthroughs in Artificial Intelligence have created the promise of opportunities with an important economic value in a broad diversity of sectors. Therefore AI has become a strategic area for many companies around the world. In his talk Alejandro will discuss some good practices and learnings of moving complex AI systems into production. Thank you Filip Bruyneel and Lauren Van Den Berghe for organising this session.
While her peers reveled in an unprecedented virtual school year, the self-described "technology enthusiast," Harita Suresh, 13, was bored. She decided on an online course and settled on IBM Skills Network's "AI chatbots without programming." She lacked experience with artificial intelligence, but was eager to learn through the self-paced course. Harita is more than a little familiar with tech, "I have been interested in technology since I was 5," she said. "My first coding challenge was the Lightbot Hour of Code. I was fascinated that the code I wrote could control the actions of the characters on screen. Since then, I pursued coding on multiple platforms like code.org, The more I learned about tech, the more I wanted to know. In fifth grade, I took a Python programming course offered by Georgia Tech."
ASAPP founder Gustavo Sapoznik developed software that trains customer-service reps to be "radically" more productive, winning the young startup an $800 million valuation. If you've ever felt your blood boil after sitting on hold for 40 minutes before reaching an agent . . . A customer-service representative for JetBlue, for instance, might have to flip rapidly among a dozen or more computer programs just to link your frequent-flier number to a specific itinerary. "Imagine that cognitive load, while you have someone screaming at you or complaining about some serious problem, and you're swiveling between 20 screens to see which one you need to be able to help this person," says Gustavo Sapoznik, 34, the founder and CEO of ASAPP, a New York City–based developer of AI-powered customer-service software. Sapoznik remembers just such a scene while shadowing a call-center agent at a "very large" company (he won't name names), watching the worker navigate a "Frankenstack" patchwork of software, entering a caller's information into six different billing systems before locating it.
Most people surveyed about autonomous are comfortable with the technology and yet billions continue ... [ ] to be invested in the technology. In a recent survey by Myplanet of various technologies, "autonomous driving" came in as the most uncomfortable of the thirty-five technologies at 66.8% of the Americans surveyed. To put that in perspective, one of the technologies near the middle of the pack was "surgical robot" at 42% negative, which translates into "I'd rather your'bot cuts me open than have it drive me to the corner store." As summarized well by Jason Cottrell, Myplanet CEO, "Customers have made up their minds about autonomous driving and it's skewed heavily to the negative." Other studies, in fact, corroborate that level of fear (e.g.
PyCaret is a python open source low-code machine learning library created by Moez Ali and released in April 2020. It is literally a low-code library which allows to create an entire machine learning pipeline with very few lines of code. PyCaret is essentially a wrapper built on common python machine learning libraries such as scikit-learn, XGBOOST and many more. What PyCaret achieves is a higly simple yet functional syntax. For instance, we can compare 18 classification models with 1 line of code.