Did you know that half of today's jobs will become obsolete in the next 15 years? According to Kai-fu Lee, one of China's leading artificial intelligence (AI) experts, nearly 50% of all jobs today will be automated by 2034. As technology becomes more advanced many jobs traditionally performed by humans are being taken up by machines. Just a few years ago, self-driving vehicles were pure science fiction. Today, thanks to massive advancements in technology, there is a global movement to make autonomous vehicles a norm on our roads.

Amazon Web Services' facial recognition technology, Rekognition, can now detect fear. At least, that's according to Amazon -- but given that the tech has been proven inaccurate again and again, it's hard to say whether the newly announced capability actually works as advertised. On Monday, Amazon Web Services (AWS) published a blog post detailing several updates to its controversial tech, including improved accuracy at detecting seven emotions on a person's face -- happy, sad, angry, surprised, disgusted, calm, and confused -- with fear added as an eighth emotion. The company also claimed it had improved Rekognition's ability to estimate a person's gender as well as their age, with the tech now providing a narrower range of possibilities for most age groups. The announcement of the updates was met by a wave of criticism online, with cloud computing economist Corey Quinn noting that the move seems incredibly ill-timed given recent controversy surrounding Rekognition.

With the help of Artificial Intelligence(AI), researchers have developed a neural network approach that can accurately identify congestive heart failure with 100 per cent accuracy through analysis of just one raw electrocardiogram (ECG) heartbeat. Congestive heart failure (CHF) is a chronic progressive condition that affects the pumping power of the heart muscles. Associated with high prevalence, significant mortality rates and sustained healthcare costs, clinical practitioners and health systems urgently require efficient detection processes. The researchers have worked to tackle these important concerns by using Convolutional Neural Networks (CNN) – hierarchical neural networks highly effective in recognising patterns and structures in data. "We trained and tested the CNN model on large publicly available ECG datasets featuring subjects with CHF as well as healthy, non-arrhythmic hearts. Our model delivered 100 per cent accuracy: by checking just one heartbeat we are able detect whether or not a person has heart failure," said study researcher Sebastiano Massaro, Associate Professor at the University of Surrey in the UK.

During 15-17 September, RaySearch will exhibit its latest advances in oncology software at the American Society for Radiation Oncology (ASTRO) 2019 annual meeting in Chicago, USA. On show will be new development in machine learning technology and automation in the RayStation* treatment planning system and the RayCare * oncology information system. Machine learning capabilities were added already in RayStation 8B and are being continuously improved. RaySearch has now been granted FDA 510(k) clearance for deep learning organ segmentation and for machine learning automated planning for a key model. Several planning models are being validated for future FDA 510(k) clearance.

Eager to make extra money on the side, Washington, D.C., resident Paula Alves Silva turned to a gig emblematic of the digital age: She recorded sentences read aloud in the comfort of her home to help train artificial intelligence (AI) software. Silva completed the tasks in her native Portuguese tongue for Seattle-based startup DefinedCrowd, which develops machine learning algorithms that power products for businesses including heavyweights MasterCard and BMW. Such recordings could be used in voice recognition products introduced in new countries, or to train existing systems to recognize non-native speakers or regional accents, the company says. Silva earned $20 -- from 8 to 33 cents per sentence -- and considered that satisfactory given the short amount of time it took to complete the tasks. The knowledge that her task would contribute to a new artificial intelligence system was a bonus, she said.

Accenture has acquired Spanish analytics services firm Pragsis Bidoop. Headquartered in Madrid with additional offices in Barcelona and the UK, Pragsis will add more than 200 employees with deep machine learning, artificial intelligence and big data engineering experience to Accenture's Applied Intelligence business. Accenture Applied Intelligence serves the European and Latin American markets by helping enterprise clients scale their deployments of AI, analytics and automation to make improved data-driven decisions. Founded in 2004, Pragsis Bidoop has a diversified client base of Ibex-35 companies in Spain and multinational corporations with a presence in Mexico, Brazil and other Latin America markets. The company specialises in service provisioning for corporations in a range of industries, from banking and insurance to retail, energy, railways, the public sector and pharma.

In 2018, Brazil had over 230 million cows chewing their cuds – about 22% of the 1.02 billion cows living on this planet – and consequently was the world's largest exporter of beef that year. With the country's revenues from agriculture reaching $84.6 billion, it makes sense that there would be some agriculture technology (agtech) startups cropping up in the Land of the Holy Cross. The Brazilian Institute of Geography and Statistics has noted the efforts this South American country has been making to advance the use of technology in farming. For example, tractor use in the country has grown by almost 50% in the past decade while crop irrigation use has increased by 52%. The agricultural sector is now working hand-in-hand with the tech world to capture big data and turn it into insights for "precision farming," something we talked about in our article on 6 IoT in Agriculture Solutions from AgTech Startups.

Roughly six years ago, former Israel chief technology officer Yonatan Adiri cofounded Healthy.io, a digital health care startup leveraging AI to bring professional-grade medical imaging to the homes of those with chronic kidney disease. Its 10-parameter digital testing kit -- Dip.io -- enables patients to collect and analyze urine samples with nothing more than a smartphone app, a dip stick, and a color-coded slide. It's taken off like wildfire. Following on the heels of an $18 million series B raise in February, Healthy.io CEO Adiri said the fresh capital, which brings Healthy.io's total raised to about $90 million, will accelerate Healthy.io's

Machine learning is a powerful paradigm many organizations are utilizing to derive insights and add features to their applications, but using it requires skills, data, and effort. Explorium, a startup from Israel, has just announced $19 million of funding to lower the barrier on all of the above. The funding announced today comprises a seed round of $3.6 million led by Emerge with the participation of F2 Capital and a $15.5 million Series A led by Zeev Ventures with the involvement of the seed investors. Explorium was founded by Maor Shlomo, Or Tamir, and Omer Har, three Israeli tech entrepreneurs, who previously led large-scale data mining and optimization platforms for big data-based marketing leaders. "We are doing for machine learning data what search engines did for the web," said Explorium co-founder and CEO Maor Shlomo.