The technique uses attachments that can be inexpensively produced with a 3-D printer, the study said.

“Using deep learning, we set out to bridge the gap in image quality between inexpensive mobile phone-based microscopes and gold-standard bench-top microscopes that use high-end lenses,” said one of the lead researchers Aydogan Ozcan, Professor at the University of California, Los Angeles, US.

“We believe that our approach is broadly applicable to other low-cost microscopy systems that use, for example, inexpensive lenses or cameras, and could facilitate the replacement of high-end bench-top microscopes with cost-effective, mobile alternatives,” Ozcan added.

He said the new technique could find numerous applications in global health, telemedicine and diagnostics-related applications.

Cameras on today’s smartphones are designed to photograph people and scenery, not to produce high-resolution microscopic images.

So the researchers developed an attachment that can be placed over the smartphone lens to increase the resolution and the visibility of tiny details of the images they take, down to a scale of approximately one millionth of a metre.

But that only solved part of the challenge, because no attachment would be enough to compensate for the difference in quality between smartphone cameras’ image sensors and lenses and those of high-end lab equipment.

The new technique compensates for the difference by using AI to reproduce the level of resolution and colour details needed for a laboratory analysis.

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It has two experiences to enjoy and the third one is for developers to help them create their own experience.

The first two experiences are called “Talk to Books” in which users can explore a new way to interact with books, and “Semantris” where people can play word association games powered by semantic search.

In “Talk to Books” experience, users can simply type in a statement or a question and the AI will find whole sentences in books related to what they have typed.

Google Research Director of Engineering Ray Kurzweil and Product Manager Rachel Bernstein said the system does not depend on keyword matching.

They trained its AI by feeding it a “billion conversation-like pairs of sentences,” so it can learn to identify what a good response looks like.

For example, if you type “Best detective in the world”, the AI responds with several paragraphs and sentences that are related to the word “detective”.

This way users can find exact lines from books which they mildly remember.

The second section Semantris offers word association games like a Tetris-like break-the-blocks experience.

The AI would display random blocks with text written on them. Users have to “break” those blocks by typing a word which can relate to any of the text written on them.

For example, if the AI displays “Football” on any block, users could write “Lionel Messi” in the space provided below the blocks.

The AI processes and matches the word with the text displayed on the blocks. Once the AI matches with the word, it “breaks” the block and some points are awarded.

According to Engadget, the development in word vector, an AI-training model that enables algorithms to learn relationships between words based on actual language usage, led to the advancement in natural language processing over the past few years.

Kurzweil and Bernstein said that these websites show how AIs’ “new capabilities can drive applications that were not possible before”.

They said other potential applications include “classification, semantic similarity, semantic clustering, whitelist applications (selecting the right response from many alternatives) and semantic search (of which Talk to Books is an example).”

Google CEO Sundar Pichai has been “betting big” on advances in AI and machine learning.

Earlier this year, Pichai said that AI is one of the most profound things that humanity is working on right now and compared it to basic utilities in terms of its importance.

”AI is ‘one of the most important things that humanity is working on. It’s more profound than, I don’t know, electricity or fire,” The Verge quoted Pichai as saying.

Pichai also said that AI could be used to help solve climate change issues or to cure cancer.

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All five of these objects are rocky bodies known to have atmospheres and are among the most potentially habitable objects in the Solar System.

“We’re currently interested in these ANNs for prioritising exploration for a hypothetical, intelligent, interstellar spacecraft scanning an exoplanet system at range,” said Christopher Bishop from the varsity.

“We’re also looking at the use of large area, deployable, planar Fresnel antennas to get data back to Earth from an interstellar probe at large distances. This would be needed if the technology is used in robotic spacecraft in the future,” Bishop added.

ANNs are systems that attempt to replicate the way the human brain learns.

Atmospheric observations—known as spectra—of the five Solar System bodies are presented as inputs to the network, which is then asked to classify them in terms of the planetary type.

As life is currently known only to exist on Earth, the classification uses a “probability of life” metric which is based on the relatively well-understood atmospheric and orbital properties of the five target types.

“Given the results so far, this method may prove to be extremely useful for categorising different types of exoplanets using results from ground-based and near-Earth observatories,” said Angelo Cangelosi, the supervisor of the project.

The technique may also be ideally suited to selecting targets for future observations, given the increase in spectral detail expected from upcoming space missions such European Space Agency’s Ariel Space Mission and NASA’s James Webb Space Telescope.

The work was presented at the European Week of Astronomy and Space Science (EWASS) in Liverpool.

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“While the trained eyes of expert pathologists are able to gauge the severity of disease and detect nuances of kidney damage with remarkable accuracy, such expertise is not available in all locations, especially at a global level,” said Vijaya B. Kolachalama, PhD, Assistant Professor at the Boston University.

The machine learning framework based on convolutional neural networks (CNN) relied on pixel density of digitised images, while the severity of disease was determined by several clinical laboratory measures and renal survival.

CNN model performance was then compared with that of the models generated using the amount of fibrosis reported by a nephropathologist as the sole input and corresponding lab measures and renal survival as the outputs.

“In essence, our model has the potential to act as a surrogate nephropathologist, especially in resource-limited settings,” Kolachalama added.

Each year, kidney disease kills more people than breast or prostate cancer, and the overall prevalence of chronic kidney disease in the general population is approximately 14 per cent.

The new model, appearing in the journal Kidney International Reports, has both diagnostic and prognostic applications and may lead to the development of a software application for diagnosing kidney disease and predicting kidney survival, the researchers said.

To test the feasibility of applying this technology to the analysis of routinely obtained kidney biopsies, the researchers performed a proof of principle study on kidney biopsy sections with various amounts of kidney fibrosis (also commonly known as scarring of tissue).

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One of the two newly-discovered planets is Kepler-90i—a sizzling hot, rocky planet that orbits its star once every 14.4 days. This marks the discovery of an eighth planet circling Kepler-90, a Sun-like star 2,545 light-years from Earth – making it the first known eight-planet system outside of our own.

“Our solar system now is tied for most number of planets around a single star, with the recent discovery of an eighth planet circling Kepler-90,” NASA said in a statement on Friday.

“It (Kepler-90i) is 30 per cent larger than Earth, and with a surface temperature of approximately 800 degree F (426.6 degrees Celsius) — not ideal for your next vacation. It also orbits its star every 14 days, meaning you would have a birthday there just about every two weeks,” the researchers said in a Google blog post.

The discovery came after Christopher Shallue and Andrew Vanderburg at Google AI trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler—the minuscule change in brightness captured when a planet passed in front of, or transited, a star.

Inspired by the way neurons connect in the human brain, this artificial “neural network” sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco.

While machine learning has previously been used in searches of the Kepler database, this research demonstrates that neural networks are a promising tool in finding some of the weakest signals of distant worlds.

Shallue, a senior software engineer with Google AI research team, came up with the idea to apply a neural network to the Kepler data.

“In my spare time, I started googling for ‘finding exoplanets with large data sets’ and found out about the Kepler mission and the huge data set available,” said Shallue.

First, the researchers trained the neural network to identify transiting exoplanets using a set of 15,000 previously-vetted signals from the Kepler exoplanet catalogue.

In the test set, the neural network correctly identified true planets and false positives 96 per cent of the time.

Then, with the neural network having “learned” to detect the pattern of a transiting exoplanet, the researchers directed their model to search for weaker signals in 670 star systems that already had multiple known planets.

Their assumption was that multiple-planet systems would be the best places to look for more exoplanets.

“We got lots of false positives of planets, but also potentially more real planets,” said Vanderburg.

“It’s like sifting through rocks to find jewels. If you have a finer sieve then you will catch more rocks but you might catch more jewels, as well.”

Kepler-90i wasn’t the only jewel this neural network sifted out. In the Kepler-80 system, they also found a sixth planet.

This one, the Earth-sized Kepler-80g, and four of its neighbouring planets form what is called a resonant chain where planets are locked by their mutual gravity in a rhythmic orbital dance.

The result is an extremely stable system, similar to the seven planets in the TRAPPIST-1 system.

The findings will be published in the forthcoming issue of The Astronomical Journal.

The researchers said they plan to apply their neural network to Kepler’s full set of more than 150,000 stars.

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Rapid identification and delivery of antibiotic medications is the key to treating bloodstream infections, which can kill up to 40 per cent of patients who develop them.

In the study, led by scientists Beth Israel Deaconess Medical Centre (BIDMC) in Boston, used an automated microscope designed to collect high-resolution image data from microscopic slides.

A convolutional neural network (CNN) — a class of artificial intelligence modelled on the mammalian visual cortex and used to analyse visual data—was trained to categorise bacteria based on their shape and distribution.

To train the AI system, the scientists fed the neural network more than 100,000 images from blood samples.

The machine intelligence learned how to sort the images into the three categories of bacteria—rod-shaped, round clusters, and round chains or pairs—ultimately achieving nearly 95 per cent accuracy, the researchers said.

“This marks the first demonstration of machine learning in the diagnostic area,” said James Kirby, Director at the Clinical Microbiology Laboratory at BIDMC.

“With further development, we believe this technology could form the basis of a future diagnostic platform that augments the capabilities of clinical laboratories, ultimately speeding the delivery of patient care,” Kirby added, in the paper published in the Journal of Clinical Microbiology.

Automated classification can also ameliorate the shortage of human technologists by helping them work more efficiently, “conceivably reducing technologist read time from minutes to seconds”, Kirby suggested.

In addition, the new tool could also have applications in microbiology training and research.

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In Karnataka, the government can get price forecasts for essential commodities such as tur (split red gram) three months in advance for planning the Minimum Support Price (MSP).

“Sowing date as such is very critical to ensure that farmers harvest a good crop. And if it fails, it results in loss as a lot of costs are incurred for seeds, as well as the fertilizer applications,” Suhas P. Wani, Director, Asia Region, of the International Crop Research Institute for the Semi-Arid Tropics (ICRISAT), said in a Microsoft blog post.

The non-profit ICRISAT conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world.

In collaboration with ICRISAT, Microsoft has developed an AI-Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning and Power BI.

“The app sends sowing advisories to participating farmers on the optimal date to sow. The best part – the farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they need is a feature phone capable of receiving text messages,” the company said.

To calculate the crop-sowing period, historic climate data spanning over 30 years – from 1986 to 2015 – for the Devanakonda area in Andhra Pradesh was analysed using AI.

To determine the optimal sowing period, the Moisture Adequacy Index (MAI) was calculated.

MAI is the standardised measure used for assessing the degree of adequacy of rainfall and soil moisture to meet the potential water requirement of crops.

This data is then downscaled to build predictability and guide farmers to pick the ideal sowing week.

This year, ICRISAT has scaled sowing insights to 4,000 farmers across Andhra Pradesh and Karnataka for the Kharif crop cycle (rainy season).

Predictive analysis in agriculture is not limited to crop growing alone.

The Karnataka government will start using price forecasting for agricultural commodities, in addition to sowing advisories for farmers in the state.

Commodity prices for items such as tur, of which Karnataka is the second largest producer, will be predicted three months in advance for major markets in the state, Microsoft said.

Microsoft has developed a multivariate agricultural commodity price forecasting model to predict future commodity arrival and the corresponding prices.

The model uses remote sensing data from geo-stationary satellite images to predict crop yields through every stage of farming.

The model currently being used to predict the prices of tur, is scalable, and time efficient and can be generalised to many other regions and crops.

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The Federal Communications Commission (FCC) permits a cognitive radio to use the frequency while unused by its primary user until the user becomes active again.

“Modern space communications systems use complex software to support science and exploration missions. By applying AI and machine learning, satellites control these systems seamlessly, making real-time decisions without awaiting instruction,” Janette C. Briones, Principal Investigator at NASA’s Glenn Research Centre in Cleveland, Ohio, said in a statement on Saturday.

The recent development of cognitive technologies is a new thrust in the architecture of communications systems.

“We envision these technologies will make our communications networks more efficient and resilient for missions exploring the depths of space. By integrating AI and cognitive radios into our networks, we will increase the efficiency, autonomy and reliability of space communications systems,” added Briones.

For NASA, the space environment presents unique challenges that cognitive radio could mitigate.

Space weather, electromagnetic radiation emitted by the Sun and other celestial bodies, fills space with noise that can interrupt certain frequencies.

The Space Communications and Navigation (SCaN) Testbed aboard the International Space Station (ISS) provides engineers and researchers with tools to test cognitive radio in the space environment.

The testbed houses three software-defined radios in addition to a variety of antennas and apparatus that can be configured from the ground or other spacecraft.

“The testbed keeps us honest about the environment in orbit. While it can be simulated on the ground, there is an element of unpredictability to space. The testbed provides this environment, a setting that requires the resiliency of technology advancements like cognitive radio,” explained Dave Chelmins, Project Manager for the SCaN Testbed and cognitive communications.

In the future, a NASA cognitive radio could even learn to shut itself down temporarily to mitigate radiation damage during severe space weather events.

Adaptive radio software could circumvent the harmful effects of space weather, increasing science and exploration data returns.

A cognitive radio network could also suggest alternate data paths to the ground, said NASA.

The cognitive radio’s AI could also allocate ground station downlinks just hours in advance, as opposed to weeks, leading to more efficient scheduling.

Additionally, cognitive radio may make communications network operations more efficient by decreasing the need for human intervention.

An intelligent radio could adapt to new electromagnetic landscapes without human help and predict common operational settings for different environments, automating time-consuming processes previously handled by humans.

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It would also allow devices to operate independent of the internet while using AI that performs almost as well as tethered neural networks.

“We feel this has enormous potential. This could be an enabler in many fields where people are struggling to get deep-learning AI in an operational form,” said Alexander Wong, Professor at the University of Waterloo in Canada.

Deep-learning AI, which mimics the human brain by processing data through layers and layers of artificial neurons, typically requires considerable computational power, memory and energy to function.

However, the new deep-learning AI responds by adapting and changing itself to keep functioning each time computational power and memory are taken away.

“These networks evolve themselves through generations and make themselves smaller to be able to survive in these environments,” explained another researcher Mohammad Javad Shafiee, Professor at the varsity.

The researchers, who presented the results during the International Conference on Computer Vision in Italy, noted that that when put on a chip and embedded in a smartphone, such compact AI could run its speech-activated virtual assistant and other intelligent features, greatly reducing data usage and operating without internet service.

Other potential applications range from use in low-cost drones and smart grids, to surveillance cameras and manufacturing plants, where there are significant issues around streaming sensitive or proprietary data to the cloud, the researchers said.

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Toyota Motor Corp, Japan’s largest carmaker by volume, unveiled its new concept car “Fine-Comfort Ride” featuring hydrogen fuel cell, and three “Concept-i” vehicles with automated driving and AI technologies.

The scandal-hit Nissan Motor Co and Honda Motor Co both showcased their new electric vehicles, including the “Honda Sports EV Concept” car which is equipped with AI technologies and can interact with the driver.

The show also features connectivity technologies, with an event titled “Tokyo Connected Lab 2017” offering visitors virtual experiences of driving in connected cars in a futuristic society through VR headsets.

The show will be open to the public from October 28 through November 5 at Tokyo Big Sight.

Though one of the world’s five major motor shows, the show suffered a decline in popularity in recent years as automakers shifted their attention to the emerging markets.

The biennial show attracted 2.02 million visitors in 1991 at its peak but only some 810,000 visitors in 2015.

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