Amazon says its increasingly popular facial recognition software has learned a few new tricks, including the ability to discern when someone is scared. The software, called'Rekognition', has added'fear' to its list of detectable emotions which already includes'Happy', 'Sad', 'Angry', 'Surprised', 'Disgusted', 'Calm' and'Confused,' said Amazon in an announcement earlier this week. In addition to its emotion capabilities, Amazon says its has also improved Rekognition's ability to identify gender and age more accurately. Amazon's facial recognition software can now detect'fear' and better glean age and gender according to an announcement by the company. The improved age features offer smaller age ranges across the spectrum and also more accurate range predictions, said the company.

You could be forgiven for thinking that AI will soon replace human physicians based on headlines such as "The AI Doctor Will See You Now," "Your Future Doctor May Not Be Human," and "This AI Just Beat Human Doctors on a Clinical Exam." But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of AI services working alongside human clinicians. There is no shortage of optimism about AI in the medical community. But many also caution the hype surrounding AI has yet to be realized in real clinical settings. There are also different visions for how AI services could make the biggest impact.

Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost could lead to reduced healthcare costs for a patient. In addition, a precisely learned molecule representation in a DTI model could contribute to developing personalized medicine, which will help many patient cohorts. In this paper, we propose a new molecule representation based on the self-attention mechanism, and a new DTI model using our molecule representation. The experiments show that our DTI model outperforms the state of the art by up to 4.9% points in terms of area under the precision-recall curve. Moreover, a study using the DrugBank database proves that our model effectively lists all known drugs targeting a specific cancer biomarker in the top-30 candidate list.

Deepfakes are video manipulations that can make people say seemingly strange things. Barack Obama and Nicolas Cage have been featured in these videos. A video of President Donald Trump singing "America the Beautiful" received over 50,000 views on Instagram when it was posted just before the July 4th holiday. Did the president really record a video of himself singing and post it to social media? But with the growing prevalence of altered videos and intentional mischief, the question "Is it real?" is one we will need to ask with more regularity.

Scalable cognitive solutions that meet these objectives must leverage machine learning to manage the vast amount of data that is produced by security sensors. Security expertise, data science and the math behind machine learning are all essential to developing the complex mechanisms, timing and features of machine learning systems. Thus, taking the right action by creating security tactics enabled by machine learning is dependent on three things: resources, confidence in the science, and actionability. In this paper, Jon Ramsey, Secureworks CTO, provides his vision of Machine Learning and how these three things above can enable "Smart Security" to benefit CIOs and businesses.

There is growing pressure for more details about the use of facial recognition in London's King's Cross to be disclosed after a watchdog described the deployment as "alarming". Developer Argent has confirmed it uses the technology to "ensure public safety" but did not reveal any details. It raises the issue of how private land used by the public is monitored. The UK's biometrics commissioner said the government needed to update the laws surrounding the technology. Argent is responsible for a 67-acre site close to King's Cross station.

High-profile data breaches of enterprise companies and large government agencies gain a lot of news coverage. But does the lack of reporting involving small and medium-sized businesses mean that, for them, the cybersecurity risk is much smaller? The reality, however, is quite the opposite. SMBs have information and credentials that are indeed valuable for cybercriminals, including: employee and customer records, access to business financial information including bank accounts, and access to larger companies and their networks through the supply chain. Read the report to learn more about how and where cybercriminals are likely to strike and how to protect your business from cyberattacks using a layered security approach.

In the policies promoted by the European Union, an intimate connection between artificial intelligence and open data has been considered. In this regard, as we highlighted, open data is essential for the proper functioning of artificial intelligence, since the algorithms must be fed by data whose quality and availability is essential for its continuous improvement, as well as to audit its correct operation. Artificial intelligence entails an increase in the sophistication of data processing, since it requires greater precision, updating and quality, which, on the other hand, must be obtained from very diverse sources to increase the quality of the algorithms results. Likewise, an added difficulty is the fact that processing is carried out in an automated way and must offer precise answers immediately to face changing circumstances. Therefore, a dynamic perspective that justifies the need for data -not only to be offered in open and machine-readable format, but also with the highest levels of precision and disaggregation- is needed.

It's clear that a lot of action to reduce emissions and climate change will play out in urban areas, where most people in the world live, and an even higher proportion of economic activity happens. Save the city, save the world. But it's hard to make a substantial change to how a city runs. Successful cities are generally already going through continual change. Failing cities generally aren't managing to change to meet new conditions.

There is a lively debate all over the world regarding AI's perceived "black box" problem. Most profoundly, if a machine can be taught to learn itself, how does it explain its conclusions? This issue comes up most frequently in the context of how to address possible algorithmic bias. One way to address this issue is to mandate a right to a human decision per the General Data Protection Regulation's (GDPR) Article 22. Here in the United States, Senators Wyden and Booker propose in the Algorithmic Accountability Act that companies be compelled to conduct impact assessments. Auditability, explainability, transparency and replicability (reproducibility) are often suggested as means of avoiding bias.