1. Should Robots Have a Face?

When Tina Sorg first saw the robot rolling through her Giant supermarket in Harrisburg, Pa., she said to herself, “That thing is a little weird.” Programmed to detect spills and debris in the aisles, the robot looked like an inkjet printer with a long neck. “It needed personality,” said Ms. Sorg, 55, who manages the store’s beer and wine department.

Author:

Read More On: The New York Times

2. Deep learning advances are boosting computer vision — but there’s still clear limits

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Since the early days of artificial intelligence, computer scientists have been dreaming of creating machines that can see and understand the world as we do. The efforts have led to the emergence of computer vision, a vast subfield of AI and computer science that deals with processing the content of visual data.

Author: Ben Dickson 

Read More On: The Next Web

3. Quantum Computers Could Be True Randomness Generators

Say the words “quantum supremacy” at a gathering of computer scientists, and eyes will likely roll. The phrase refers to the idea that quantum computers will soon cross a threshold where they’ll perform with relative ease tasks that are extremely hard for classical computers. Until recently, these tasks were thought to have little real-world use, hence the eye rolls.

Author: Anil Ananthaswamy

Read More On: Wired

4. Oracle Cloud Infrastructure Finally Gets Data Science And ML Services

Oracle recently announced the availability of Cloud Data Science PlatformAccording to Oracle, the key differentiating factor for its data science platform is team collaboration features and tight integration with a variety of data sources available in OCI. According to Oracle, the key differentiating factor for its data science platform is team collaboration features and tight integration with a variety of data sources available in OCI.

Author: Janakiram MSV

Read More On: Forbes

5. A human-machine collaboration to defend against cyberattacks

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.

Author: Zach Winn 

Read More On: MIT News

6. Your next tire change could be performed by a robot

Waiting in a service station waiting room purgatory one day, Victor Darolfi had a simple thought. “I sat at America’s Tires for three hours and thought, hey, we use robots to put tires on at the factory,” the founder explains. “Why don’t we bring robots into the service industry?”

Author: Brian Heater

Read More On: TechCrunch

7. AI is not just another technology project

AI, unlike any other initiative is a business transformation enabler and not another technology system implementation that business users need to be trained on. Traditionally, businesses choose either the classic waterfall approach of linear tasks, or the agile approach, where teams review and evaluate solutions as they are tested out.

Author: Bret Greenstein

Read More On: VentureBeat

8. Deep learning AI discovers surprising new antibiotics

Imagine you’re a fossil hunter. You spend months in the heat of Arizona digging up bones only to find that what you’ve uncovered is from a previously discovered dinosaur. That’s how the search for antibiotics has panned out recently. The relatively few antibiotic hunters out there keep finding the same types of antibiotics.

Read More On: The Conversation

9. Choosing Between Rule-Based Bots And AI Bots

Until a decade ago, the only option people had to reach out to a company was to call or email their customer service team. Now, companies offer a chat team to provide better round-the-clock customer service. According to a Facebook-commissioned study by Nielsen, 56% of people would prefer to message rather than call customer service, and that’s where bots come into play.

Author: Naveen Joshi

Read More On: Forbes

10. Facebook’s AI speeds up natural language processing without additional training

Natural language models typically have to solve two tough problems: mapping sentence prefixes to fixed-sized representations and using the representations to predict the next word in the text. In a recent paper, researchers at Facebook AI Research assert that the first problem — the mapping problem — might be easier than the prediction problem, a hypothesis they build upon to augment language models with a “nearest neighbors” retrieval mechanism.

Author: Kyle Wiggers

Read More On: VentureBeat