neural network


Synechron launches AI data science accelerators for FS firms

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These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad. With this, Synechron's Global Accelerator programs now includes over 50 Accelerators for: Blockchain, AI Automation, InsurTech, RegTech, and AI Data Science and a dedicated team of over 300 employees globally.


Researchers develop offline speech recognition that's 97% accurate

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Typically, deep learning approaches to voice recognition -- systems that employ layers of neuron-mimicking mathematical functions to parse human speech -- lean on powerful remote servers for bulk of processing. But researchers at the University of Waterloo and startup DarwinAI claim to have pioneered a strategy for designing speech recognition networks that not only achieves state-of-the-art accuracy, but which produces models robust enough to run on low-end smartphones. They describe their method in a paper published on the preprint server Arxiv.org It builds on work by Amazon's Alexa Machine Learning team, which earlier this year developed navigation, temperature control, and music playback algorithms that can be performed locally; Qualcomm, which in May claimed to have created on-device voice recognition models that are 95 percent accurate; Dublin, Ireland startup Voysis, which in September announced an offline WaveNet voice model for mobile devices; and Intel. "In this study, we explore a human-machine collaborative design strategy for building low-footprint [deep neural network] architectures for speech recognition through a marriage of human-driven principled network design prototyping and machine-driven design exploration," the researchers wrote.


3 Ways AI Is Changing Healthcare

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THE CURRENT U.S. HEALTH CARE PICTURE is pretty bleak: more than 12 million serious diagnostic errors each year, a third of the $3.6 trillion spent attributed to waste, reduction in life expectancy for what will be three years in a row (which is unpre cedented), and peak levels of physician burnout, depression, and suicide. That's all happening at a time when there is more medical data per individual than ever, imagined with wearable sensor physiology, scan anatomy (above), DNA sequencing, gut microbiome biology, just to name a few layers. Enter deep-learning A.I., with neural networks that will impact every type of clinician, from helping to accurately read scans, slides, skin lesions, eyegrounds, and more, to health systems, promoting the use of remote monitoring that ultimately obviates the need for regular hospital rooms, and at the consumer level, by providing a virtual medical coach to better manage or even prevent diseases. But it's our best shot to deal with all of the formidable challenges: to use the wealth of data to reduce errors and waste, and the gift of time to markedly improve the clinician-patient relationship. IN JUST THE PAST few years, there have emerged credible if still-in-the-works A.I.-powered technologies that can read radiology scans (like Imagen), identify tumors and track the spread of cancer (Arterys), detect eye conditions using retinal imaging (Google's DeepMind), flag dangerously abnormal potassium levels via a "bloodless blood test" (Mayo Clinic Ventures and AliveCor), and otherwise assist with the tricky business of diagnosing, or even predicting, disease.


Machine Learning Fun and Easy - YouTube

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Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.


Variational Autoencoders Explained – Towards Data Science

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If such a model is trained on natural looking images, it should assign a high probability value to an image of a lion. An image of random gibberish on the other hand should be assigned a low probability value. The VAE model can also sample examples from the learned PDF, which is the coolest part, since it'll be able to generate new examples that look similar to the original dataset! The input to the model is an image in a 28 28 dimensional space (ℝ[28 28]). The model should estimate a high probability value if the input looks like a digit.


Scientists may now be able to grow a brain using human neurons

FOX News

Scientists may have made a major leap forward on the path to growing a fully-formed human brain in the lab. According to a new study, researchers at Tufts University have now grown a 3D tissue model of the brain using human neurons, providing them with a better opportunity to study abnormal brain cells. Though brain tissue cells have been cultured for years under laboratory conditions, this technique employs a three-dimensional scaffold of functional neural tissue. The researchers used human induced pluripotent stem cells or iPSCs taken from a variety of sources to create "brain-like organoids." "We found the right conditions to get the iPSCs to differentiate into a number of different neural subtypes, as well as astrocytes that support the growing neural networks," said David Kaplan from Tufts.


Synechron Launches AI Data Science Accelerators for the BFSI sector

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Synechron the global financial services consulting and technology services provider, has announced the launch of its AI Data Science Accelerators for Financial Services, Banking and Insurance (BFSI) firms. These four new solution accelerators help financial services and insurance firms solve complex business challenges by discovering meaningful relationships between events that impact one another (correlation) and cause a future event to happen (causation). Following the success of Synechron's AI Automation Program – Neo, Synechron's AI Data Science experts have developed a powerful set of accelerators that allow financial firms to address business challenges related to investment research generation, predicting the next best action to take with a wealth management client, high-priority customer complaints, and better predicting credit risk related to mortgage lending. The Accelerators combine Natural Language Processing (NLP), Deep Learning algorithms and Data Science to solve the complex business challenges and rely on a powerful Spark and Hadoop platform to ingest and run correlations across massive amounts of data to test hypotheses and predict future outcomes. The Data Science Accelerators are the fifth Accelerator program Synechron has launched in the last two years through its Financial Innovation Labs (FinLabs), which are operating in 11 key global financial markets across North America, Europe, Middle East and APAC; including: New York, Charlotte, Fort Lauderdale, London, Paris, Amsterdam, Serbia, Dubai, Pune, Bangalore and Hyderabad.


Generalized Graph Networks ups Deep Learning to next level AI - NextBigFuture.com Generalized Graph Networks ups Deep Learning to next level AI

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Deep Learning and Machine Learning has made breakthroughs in recent years. There is tens of billions of dollars going into development of the new AI. Google and Deep Mind are recognizing that Deep Learning is not going to reach human cognition. They propose using models of networks to find relations between things to enable computers to generalize more broadly about the world. Deep learning faces challenges in complex language and scene understanding, reasoning about structured data, transferring learning beyond the training conditions, and learning from small amounts of experience.


Google's DeepMind AI gains on human oncologists in planning radiation cancer treatments Industry Latest Technology News Prosyscom.tech

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More than half a million people are diagnosed with cancers of the head and neck each year, many of whom choose to undergo radiotherapy. But it's a delicate process: The surrounding tissue can be severely damaged if it isn't carefully isolated prior to treatments. In partnership with the University College London Hospital, Google subsidiary DeepMind is exploring ways artificial intelligence (AI) can aid in the segmentation process. It today announced a significant step forward in the pursuit of that vision: validation of a model that exhibits "near-human performance" on CT scans. "Automated … segmentation has the potential to address these challenges but, to date, performance of available solutions in clinical practice has proven inferior to that of expert human operators," the researchers wrote.


Deep Learning Performance Cheat Sheet – Towards Data Science

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The question that I get the most from new and experienced machine learning engineers is "how can I get higher accuracy?" Makes a lot of sense since the most valuable part of machine learning for business is often its predictive capabilities. Improving the accuracy of prediction is an easy way to squeeze more value from existing systems. The guide will be broken up into four different sections with some strategies in each. Not all of these ideas will boost performance, and you will see limited returns the more of them you apply to the same problem.