Three hundred and sixty-four days after she lost her job as a co-lead of Google's ethical artificial intelligence (AI) team, Timnit Gebru is nestled into a couch at an Airbnb rental in Boston, about to embark on a new phase in her career. Google hired Gebru in 2018 to help ensure that its AI products did not perpetuate racism or other societal inequalities. In her role, Gebru hired prominent researchers of color, published several papers that highlighted biases and ethical risks, and spoke at conferences. She also began raising her voice internally about her experiences of racism and sexism at work. But it was one of her research papers that led to her departure. "I had so many issues at Google," Gebru tells TIME over a Zoom call.

There's no doubt that the data science industry has come along way just in the last ten years, but you might be surprised that there is still a lot of growth potential in existing companies today. Perhaps one big reason for that is that we consistently face a shortage of qualified individuals, but I think another reason is that non-practitioners don't really understand the value that data science and artificial intelligence can bring. They hear the words "AI" or "machine learning" and associate those to Hollywood stereotypes like HAL from 2001: A Space Odyssey or Skynet from the Terminator movies. Of course, data science practitioners recognize that those Hollywood AIs represent a fictionalized potential for Artificial General Intelligence (AGI), but there's a lot more to this space than a talking computer. From random forest classifiers working well with structured data to deep learning working with unstructured data like text or images, there are a lot of different ways a data scientist can bring value to the table.

Huawei Consumer Business Group (BG) has announced the launch of Huawei nova 9 in Morocco, the ideal smartphone for the younger generation. "Incorporating a very rich set of innovative features and adorning itself with cutting-edge design elements, the latest addition to the nova series benefits from the presence of a very powerful camera system and receives all-new endowments that create new possibilities for users," according to Huawei. The Huawei nova 9 has a very powerful photographic device, reinforced by the presence of RYYB (CFA) color filters and an XD fusion engine, Huawei points out. The hardware-software integrated camera solution allows users to capture simply amazing images and videos even when light is lacking, Huawei says, noting that photos and videos will always be ready to be shared on devices. The smartphone also receives a magnificent 120 Hz curved screen, in addition to a very powerful processor and a very long battery life rechargeable through the 66 W Huawei SuperCharge1 system.

Experts say the technology isn't replacing human workers anytime soon. But the latest steps show warehouse robots are evolving as the computer vision and software that guide them grow more sophisticated, allowing them to take on more tasks that have been largely done by people. Puma North America Inc., a division of Puma SE, is using several robotic arms to assemble orders of clothing and shoes at a distribution center in Torrance, Calif.; the company plans to install more robots at another site outside Indianapolis. The technology from Nimble Robotics Inc., whose customers include Best Buy Co. and Victoria's Secret & Co., uses a combination of cameras, grippers and artificial intelligence to pluck items from bins that another automated system delivers to workstations usually staffed by people. Remote operators are on hand to assist if the robot has trouble picking up an object.

In 2019, a team of researchers published a meta-review of studies claiming a person's emotion can be inferred from their facial movements. They concluded that there's no evidence emotional state can be predicted from expression – regardless of whether a human or technology is making the determination. "[Facial expressions] in question are not'fingerprints' or diagnostic displays that reliably and specifically signal particular emotional states regardless of context, person, and culture," the coauthors wrote. "It is not possible to confidently infer happiness from a smile, anger from a scowl, or sadness from a frown." Alan Cowen might disagree with this assertion.

Iran-backed Yemeni fighters launched drone strikes on the United Arab Emirates that caused explosions and a deadly fire outside the capital, Abu Dhabi, ratcheting up security risks in the major oil-exporting region at a critical time. One of the biggest attacks to date on UAE soil ignited a fire at Abu Dhabi's main international airport on Monday and set fuel tanker trucks ablaze in a nearby industrial area. It took place days after Yemen's Houthi fighters warned Abu Dhabi against intensifying its air campaign against them. Crude extended gains to the highest level in seven years on Tuesday after the assaults in the UAE, OPEC's third biggest oil producer. Iran's longtime support of the Houthis means the incidents could roil regional diplomatic efforts to ease frictions and separate talks to restore Tehran's 2015 nuclear deal with world powers.

Histopathological image analysis is the gold standard to diagnose cancer. Carcinoma is a subtype of cancer that constitutes more than 80% of all cancer cases. Squamous cell carcinoma and adenocarcinoma are two major subtypes of carcinoma, diagnosed by microscopic study of biopsy slides. However, manual microscopic evaluation is a subjective and time-consuming process. Many researchers have reported methods to automate carcinoma detection and classification. The increasing use of artificial intelligence (AI) in the automation of carcinoma diagnosis also reveals a significant rise in the use of deep network models. In this systematic literature review, we present a comprehensive review of the state-of-the-art approaches reported in carcinoma diagnosis using histopathological images. Studies are selected from well-known databases with strict inclusion/exclusion criteria. We have categorized the articles and recapitulated their methods based on specific organs of carcinoma origin. Further, we have summarized pertinent literature on AI methods, highlighted critical challenges and limitations, and provided insights on future research direction in automated carcinoma diagnosis. Out of 101 articles selected, most of the studies experimented on private datasets with varied image sizes, obtaining accuracy between 63% and 100%. Overall, this review highlights the need for a generalized AI-based carcinoma diagnostic system. Additionally, it is desirable to have accountable approaches to extract microscopic features from images of multiple magnifications that should mimic pathologists' evaluations.

In doing so, however, these approaches sacrifice the sample efficiency and generalizability that a well-specified feature Whether it's semi-autonomous driving (Sadigh et al. 2016), set offers. While using an expressive function approximator recommender systems (Ziebart et al. 2008), or household to extract features and learn their reward combination at once robots working in close proximity with people (Jain et al. seems advantageous, many such functions can induce policies 2015), reward learning can greatly benefit autonomous agents that explain the demonstrations. Hence, to disambiguate to generate behaviors that adapt to new situations or human between all these candidate functions, the robot requires a preferences. Under this framework, the robot uses the person's very large amount of (laborious to collect) data, and this data input to learn a reward function that describes how they prefer needs to be diverse enough to identify the true reward. For the task to be performed. For instance, in the scenario in Fig. example, the human in the household robot setting in Figure 1 1, the human wants the robot to keep the cup away from the might want to demonstrate keeping the cup away from the laptop to prevent spilling liquid over it; she may communicate laptop, but from a single demonstration the robot could find this preference to the robot by providing a demonstration of many other explanations for the person's behavior: perhaps the task or even by directly intervening during the robot's task they always happened to keep the cup upright or they really execution to correct it.

Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations. However, pretrained LMs are well-known to be prone to intrinsic biases in their training data, which may be exacerbated by biases embedded in domain-specific language data(e.g., user reviews) used to fine-tune LMs for CRSs. We study a recently introduced LM-driven recommendation backbone (termed LMRec) of a CRS to investigate how unintended bias i.e., language variations such as name references or indirect indicators of sexual orientation or location that should not affect recommendations manifests in significantly shifted price and category distributions of restaurant recommendations. The alarming results we observe strongly indicate that LMRec has learned to reinforce harmful stereotypes through its recommendations. For example, offhand mention of names associated with the black community significantly lowers the price distribution of recommended restaurants, while offhand mentions of common male-associated names lead to an increase in recommended alcohol-serving establishments. These and many related results presented in this work raise a red flag that advances in the language handling capability of LM-drivenCRSs do not come without significant challenges related to mitigating unintended bias in future deployed CRS assistants with a potential reach of hundreds of millions of end-users.

I give a short introduction to data ethics. My focal audience is mathematicians, but I hope that my discussion will also be useful to others. I am not an expert about data ethics, and my article is only a starting point. I encourage readers to examine the resources that I discuss and to continue to reflect carefully on data ethics and on the societal implications of data and data analysis throughout their lives.