Edureka, an E-learning platform for trending technologies, today announced a partnership with E&ICT Academy, National Institute of Technology, Warangal (NITW), to launch a Post Graduate Program in Artificial Intelligence (AI) and Machine Learning (ML) that will equip professionals for the cutting -edge and industry-relevant technology. 

To address the need gap for trained AI professionals in the industry, Faculty of NIT Warangal, Edureka experts, industry veterans, a large pool of SMEs and learners have collaborated to create a PG Program that seeks to develop the next cadre of high skilled professionals and experts in the field of AI & ML. The partnership will help today's IT professionals take charge of their future and become leaders in the Artificial Intelligence (AI) Machine Learning (ML) space. As an expert in online learning, Edureka will ensure that the Program learners get the best possible learning experience. 

Talking about the partnership, Ananda Rao Ladi, Chief Learning Officer, Edureka, said, “We are excited that one of the leading institutions in the country - National Institute of Technology Warangal has chosen to partner with us. It is a known fact that the skill gap in AI is a major deterrent to its adoption across businesses. Together we will provide holistic training to individuals to help them keep up with the pace of innovation using AI and ML in the industry.”

Sharing his views on this PGP program, Dr. N.V. Ramana Rao, Director, NIT Warangal, said, "At National Institute of Technology, Warangal, one of our core principles is to share our reservoir of experience in education and knowledge for mutual enrichment in the field of technical education. In the last few months, our dedicated faculty and team of researchers in the E&ICT Academy have collaborated with Edureka to create this Course which will aid in meeting the unprecedented demand for Artificial Intelligence and Machine Learning experts in the near future. With NIT Warangal and Edureka coming together, it gives us an opportunity to bring you the best of both worlds - Our deep expertise and Edureka's effective learning system and their commitment to excellence. We are confident that participants of this program will emerge as Thought Leaders in AI-ML space and will play a pivotal role in the decades to come. I welcome all the participants and wish them a successful career ahead. Let's prepare for the future."

NITW is one of India’s leading technical institutions and has also been recognized as an Institute of National Importance by the Government of India. To be eligible for the course, candidates will have to clear a thorough online application process and levels of interview rounds. This PGP is a rigorous 9-month program with over 450 hours of intensive learning designed to make one not only ready to face the challenges of the industry but also attain excellence. Individuals will be mentored by NITW professors and on course completion, the individual will receive a PGP certificate and will be an Alumnus of the E&ICT Academy, NITW. Whether you’re a fresher or a professional, this program is designed to prepare participants with the skills that they need to rise to the top of their career in AI & ML. Along with lifetime access to the course, this program promises 100% placement assistance for all learners.

Source: ET CIO

During the past 50 years, the frequency of recorded natural disasters has surged nearly five-fold.

In this blog, I’ll be exploring how converging exponential technologies (AIroboticsdrones, sensors, networks) are transforming the future of disaster relief—how we can prevent them in the first place and get help to victims during that first golden hour wherein immediate relief can save lives.

Here are the three areas of greatest impact:

  1. AI, predictive mapping, and the power of the crowd
  2. Next-gen robotics and swarm solutions
  3. Aerial drones and immediate aid supply

Let’s dive in!

Artificial Intelligence and Predictive Mapping

When it comes to immediate and high-precision emergency response, data is gold.

Already, the meteoric rise of space-based networks, stratosphere-hovering balloons, and 5G telecommunications infrastructure is in the process of connecting every last individual on the planet.

Aside from democratizing the world’s information, however, this upsurge in connectivity will soon grant anyone the ability to broadcast detailed geo-tagged data, particularly those most vulnerable to natural disasters.

Armed with the power of data broadcasting and the force of the crowd, disaster victims now play a vital role in emergency response, turning a historically one-way blind rescue operation into a two-way dialogue between connected crowds and smart response systems.

With a skyrocketing abundance of data, however, comes a new paradigm: one in which we no longer face a scarcity of answers. Instead, it will be the quality of our questions that matters most.

This is where AI comes in: our mining mechanism.

In the case of emergency response, what if we could strategically map an almost endless amount of incoming data points? Or predict the dynamics of a flood and identify a tsunami’s most vulnerable targets before it even strikes? Or even amplify critical signals to trigger automatic aid by surveillance drones and immediately alert crowdsourced volunteers?

Already, a number of key players are leveraging AI, crowdsourced intelligence, and cutting-edge visualizations to optimize crisis response and multiply relief speeds.

Take One Concern, for instance. Born out of Stanford under the mentorship of leading AI expert Andrew Ng, One Concern leverages AI through analytical disaster assessment and calculated damage estimates.

Partnering with the cities of Los Angeles, San Francisco, and numerous cities in San Mateo County, the platform assigns verified, unique ‘digital fingerprints’ to every element in a city. Building robust models of each system, One Concern’s AI platform can then monitor site-specific impacts of not only climate change but each individual natural disaster, from sweeping thermal shifts to seismic movement.

This data, combined with that of city infrastructure and former disasters, are then used to predict future damage under a range of disaster scenarios, informing prevention methods and structures in need of reinforcement.

Within just four years, One Concern can now make precise predictions with an 85 percent accuracy rate in under 15 minutes.

And as IoT-connected devices and intelligent hardware continue to boom, a blooming trillion-sensor economy will only serve to amplify AI’s predictive capacity, offering us immediate, preventive strategies long before disaster strikes.

Beyond natural disasters, however, crowdsourced intelligence, predictive crisis mapping, and AI-powered responses are just as formidable a triage in humanitarian disasters.

One extraordinary story is that of Ushahidi. When violence broke out after the 2007 Kenyan elections, one local blogger proposed a simple yet powerful question to the web: “Any techies out there willing to do a mashup of where the violence and destruction is occurring and put it on a map?”

Within days, four ‘techies’ heeded the call, building a platform that crowdsourced first-hand reports via SMS, mined the web for answers, and—with over 40,000 verified reports—sent alerts back to locals on the ground and viewers across the world.

Today, Ushahidi has been used in over 150 countries, reaching a total of 20 million people across 100,000+ deployments. Now an open-source crisis-mapping software, its V3 (or “Ushahidi in the Cloud”) is accessible to anyone, mining millions of Tweets, hundreds of thousands of news articles, and geo-tagged, time-stamped data from countless sources.

Aggregating one of the longest-running crisis maps to date, Ushahidi’s Syria Tracker has proved invaluable in the crowdsourcing of witness reports. Providing real-time geographic visualizations of all verified data, Syria Tracker has enabled civilians to report everything from missing people and relief supply needs to civilian casualties and disease outbreaks— all while evading the government’s cell network, keeping identities private, and verifying reports prior to publication.

As mobile connectivity and abundant sensors converge with AI-mined crowd intelligence, real-time awareness will only multiply in speed and scale.

Imagining the Future….

Within the next 10 years, spatial web technology might even allow us to tap into mesh networks.

As I’ve explored in a previous blog on the implications of the spatial web, while traditional networks rely on a limited set of wired access points (or wireless hotspots), a wireless mesh network can connect entire cities via hundreds of dispersed nodes that communicate with each other and share a network connection non-hierarchically.

In short, this means that individual mobile users can together establish a local mesh network using nothing but the computing power in their own devices.

Take this a step further, and a local population of strangers could collectively broadcast countless 360-degree feeds across a local mesh network.

Imagine a scenario in which armed attacks break out across disjointed urban districts, each cluster of eye witnesses and at-risk civilians broadcasting an aggregate of 360-degree videos, all fed through photogrammetry AIs that build out a live hologram in real time, giving family members and first responders complete information.

Or take a coastal community in the throes of torrential rainfall and failing infrastructure. Now empowered by a collective live feed, verification of data reports takes a matter of seconds, and richly-layered data informs first responders and AI platforms with unbelievable accuracy and specificity of relief needs.

By linking all the right technological pieces, we might even see the rise of automated drone deliveries. Imagine: crowdsourced intelligence is first cross-referenced with sensor data and verified algorithmically. AI is then leveraged to determine the specific needs and degree of urgency at ultra-precise coordinates. Within minutes, once approved by personnel, swarm robots rush to collect the requisite supplies, equipping size-appropriate drones with the right aid for rapid-fire delivery.

This brings us to a second critical convergence: robots and drones.

While cutting-edge drone technology revolutionizes the way we deliver aid, new breakthroughs in AI-geared robotics are paving the way for superhuman emergency responses in some of today’s most dangerous environments.

Let’s explore a few of the most disruptive examples to reach the testing phase.

First up….

Autonomous Robots and Swarm Solutions

As hardware advancements converge with exploding AI capabilities, disaster relief robots are graduating from assistance roles to fully autonomous responders at a breakneck pace.

Born out of MIT’s Biomimetic Robotics Lab, the Cheetah III is but one of many robots that may form our first line of defense in everything from earthquake search-and-rescue missions to high-risk ops in dangerous radiation zones.

Now capable of running at 6.4 meters per second, Cheetah III can even leap up to a height of 60 centimeters, autonomously determining how to avoid obstacles and jump over hurdles as they arise.

Initially designed to perform spectral inspection tasks in hazardous settings (think: nuclear plants or chemical factories), the Cheetah’s various iterations have focused on increasing its payload capacity, range of motion, and even a gripping function with enhanced dexterity.

Cheetah III and future versions are aimed at saving lives in almost any environment.

And the Cheetah III is not alone. Just this February, Tokyo’s Electric Power Company (TEPCO) has put one of its own robots to the test. For the first time since Japan’s devastating 2011 tsunami, which led to three nuclear meltdowns in the nation’s Fukushima nuclear power plant, a robot has successfully examined the reactor’s fuel.

Broadcasting the process with its built-in camera, the robot was able to retrieve small chunks of radioactive fuel at five of the six test sites, offering tremendous promise for long-term plans to clean up the still-deadly interior.

Also out of Japan, Mitsubishi Heavy Industries (MHi) is even using robots to fight fires with full autonomy. In a remarkable new feat, MHi’s Water Cannon Bot can now put out blazes in difficult-to-access or highly dangerous fire sites.

Delivering foam or water at 4,000 liters per minute and 1 megapascal (MPa) of pressure, the Cannon Bot and its accompanying Hose Extension Bot even form part of a greater AI-geared system to conduct reconnaissance and surveillance on larger transport vehicles.

As wildfires grow ever more untameable, high-volume production of such bots could prove a true lifesaver. Paired with predictive AI forest fire mapping and autonomous hauling vehicles, not only will solutions like MHi’s Cannon Bot save numerous lives, but avoid population displacement and paralyzing damage to our natural environment before disaster has the chance to spread.

But even in cases where emergency shelter is needed, groundbreaking (literally) robotics solutions are fast to the rescue.

After multiple iterations by Fastbrick Robotics, the Hadrian X end-to-end bricklaying robot can now autonomously build a fully livable, 180-square-meter home in under three days. Using a laser-guided robotic attachment, the all-in-one brick-loaded truck simply drives to a construction site and directs blocks through its robotic arm in accordance with a 3D model.

Meeting verified building standards, Hadrian and similar solutions hold massive promise in the long-term, deployable across post-conflict refugee sites and regions recovering from natural catastrophes.

But what if we need to build emergency shelters from local soil at hand? Marking an extraordinary convergence between robotics and 3D printing, the Institute for Advanced Architecture of Catalonia (IAAC) is already working on a solution.

In a major feat for low-cost construction in remote zones, IAAC has found a way to convert almost any soil into a building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low GHG emissions, fire protection, air circulation, and thermal mediation, IAAC’s new 3D printed native soil can build houses on-site for as little as $1,000.

But while cutting-edge robotics unlock extraordinary new frontiers for low-cost, large-scale emergency construction, novel hardware and computing breakthroughs are also enabling robotic scale at the other extreme of the spectrum.

Again, inspired by biological phenomena, robotics specialists across the US have begun to pilot tiny robotic prototypes for locating trapped individuals and assessing infrastructural damage.

Take RoboBees, tiny Harvard-developed bots that use electrostatic adhesion to ‘perch’ on walls and even ceilings, evaluating structural damage in the aftermath of an earthquake.

Or Carnegie Mellon’s prototyped Snakebot, capable of navigating through entry points that would otherwise be completely inaccessible to human responders. Driven by AI, the Snakebot can maneuver through even the most densely-packed rubble to locate survivors, using cameras and microphones for communication.

But when it comes to fast-paced reconnaissance in inaccessible regions, miniature robot swarms have good company.

Next-Generation Drones for Instantaneous Relief Supplies

Particularly in the case of wildfires and conflict zones, autonomous drone technology is fundamentally revolutionizing the way we identify survivors in need and automate relief supply.

Not only are drones enabling high-resolution imagery for real-time mapping and damage assessment, but preliminary research shows that UAVs far outpace ground-based rescue teams in locating isolated survivors.

As presented by a team of electrical engineers from the University of Science and Technology of China, drones could even build out a mobile wireless broadband network in record time using a “drone-assisted multi-hop device-to-device” program.

And as shown during Houston’s Hurricane Harvey, drones can provide scores of predictive intel on everything from future flooding to damage estimates.

Among multiple others, a team led by Texas A&M computer science professor and director of the university’s Center for Robot-Assisted Search and Rescue Dr. Robin Murphy flew a total of 119 drone missions over the city, from small-scale quadcopters to military-grade unmanned planes. Not only were these critical for monitoring levee infrastructure, but also for identifying those left behind by human rescue teams.

But beyond surveillance, UAVs have begun to provide lifesaving supplies across some of the most remote regions of the globe. One of the most inspiring examples to date is Zipline.

Created in 2014, Zipline has completed 12,352 life-saving drone deliveries to date. While drones are designed, tested, and assembled in California, Zipline primarily operates in Rwanda and Tanzania, hiring local operators and providing over 11 million people with instant access to medical supplies.

Providing everything from vaccines and HIV medications to blood and IV tubes, Zipline’s drones far outpace ground-based supply transport, in many instances providing life-critical blood cells, plasma, and platelets in under an hour.

But drone technology is even beginning to transcend the limited scale of medical supplies and food.

Now developing its drones under contracts with DARPA and the US Marine Corps, Logistic Gliders, Inc. has built autonomously-navigating drones capable of carrying 1,800 pounds of cargo over unprecedented long distances.

Built from plywood, Logistic’s gliders are projected to cost as little as a few hundred dollars each, making them perfect candidates for high-volume remote aid deliveries, whether navigated by a pilot or self-flown in accordance with real-time disaster zone mapping.

As hardware continues to advance, autonomous drone technology coupled with real-time mapping algorithms pose no end of abundant opportunities for aid supply, disaster monitoring, and richly layered intel previously unimaginable for humanitarian relief.

Concluding Thoughts

Perhaps one of the most consequential and impactful applications of converging technologies is their transformation of disaster relief methods.

While AI-driven intel platforms crowdsource firsthand experiential data from those on the ground, mobile connectivity and drone-supplied networks are granting newfound narrative power to those most in need.

And as a wave of new hardware advancements gives rise to robotic responders, swarm technology, and aerial drones, we are fast approaching an age of instantaneous and efficiently-distributed responses in the midst of conflict and natural catastrophes alike.

Empowered by these new tools, what might we create when everyone on the planet has the same access to relief supplies and immediate resources? In a new age of prevention and fast recovery, what futures can you envision?

 

Source: Singularity Hub

In Collaboration with HuntertechGlobal

AI art is having a moment. There are record-breaking auctions, artistic controversies, and debates about the nature of creativity But here’s something new: an AI-generated sculpture made from the ground remains of the computer used to design it.

It’s the work of New York artist Ben Snell, and is currently up for sale at London auction house Phillips. It’s perhaps the third signification auction of AI art in recent months, but it’s the first sculpture to go under the hammer. Christie’s sold a printed AI portrait last Octoberwhile Sotheby’s auctioned a video installation of AI art back in March.

Snell’s piece, named Dio, follows the basic methodology of these earlier works. Machine learning algorithms are used to scan and digest a database of historical artworks, and then attempt to reproduce the data they’ve seen, with their output guided by the artist.

In the case of Dio, the training data was an archive of more than 1,000 classical sculptures (including canonical pieces such as the Discobolus and Michelangelo’s David), though Snell is keeping shtum about the contribution he made in shaping the algorithm’s output.

If you’re lucky enough to be in Italy for Milan Design Week this year, do yourself a favor and check out the world’s first “chair designed using artificial intelligence to be put into production.” With language that specific you know it must be interesting.

Kartell, Philippe Starck, and Autodesk, a 3-D software company, collaborated on the design, but ultimately the AI presented the final specifications. According to a report from Dezeen, the goal of the AI project was to create a chair as the result of communication between a machine learning system and human designers.

In a video discussing the project, Starck describes the process as being like having a conversation. He says:

Kartell, Autodesk and I asked the artificial intelligence a question: do you know how we can rest our bodies using the least amount of material?

After a bit of back and forth the human and computer mash-up settled on a final design and produced the completed products using a manufacturing process called injection molding.

The results are quite compelling if you ask us. The funky design has a bit of a 1970’s future-pop look to it. According to the humans responsible for the project, the chairs are within the standards for sturdiness and quality to be sold as furniture.

No word on whether the design will ever be sold in stores, but we hope Autodesk’s ambition and Kartell and Starck’s human creativity inspire the next generation of designers to explore this kind of man-machine hybrid design process further.

Source: The Next Web

Researchers at the University of California, Berkeley have engineered a robot ideal for artificial intelligence applications. Named Blue, the machine is low-cost and human-friendly enough to one day be a staple in every home.

Mastering human tasks

Blue was designed to use recent AI and deep reinforcement advances to learn to master such human tasks as folding laundry or making a cup of coffee.

"AI has done a lot for existing robots, but we wanted to design a robot that is right for AI," said project leader Pieter Abbeel, professor of electrical engineering and computer sciences at UC Berkeley.

"Existing robots are too expensive, not safe around humans and similarly not safe around themselves -- if they learn through trial and error, they will easily break themselves. We wanted to create a new robot that is right for the AI age rather than for the high-precision, sub-millimeter, factory automation age."

Blue's is not only durable but also affordable. In total, the robot costs less than $5,000 to manufacture and assemble.

Furthermore, Blue's arms have been ideally designed to be sensitive to outside forces. They can be very stiff or very flexible depending on what a task may require. This is very different from traditional rigid robotics focused on industrial applications. 

Rigidity versus Flexibility

 

 

"We've often described these industrial robots as moving statues," said graduatestudent David Gealy. 

"They are very rigid, meant to go from point A to point B and back to point A perfectly. But if you command them to go a centimeter past a table or a wall, they are going to smash into the wall and lock up, break themselves or break the wall. Nothing good."

Blue is designed to function in an environment where mistakes are made in order to learn from them. As such, the robot is highly sensitive to feedback, always adapting the amount of force it exerts at any given time.

"One of the things that's really cool about the design of this robot is that we can make it force-sensitive, nice and reactive, or we can choose to have it be very strong and very rigid," added Gealy. 

To achieve these capabilities, the researchers had to decide what features Blue needed and what the robot could do without. As such, they gave Blue joints that can move in the same directions as a human arm but lack some of the strength and precision of a typical robot.

"What we realized was that you don't need a robot that exerts a specific force for all time, or a specific accuracy for all time. With a little intelligence, you can relax those requirements and allow the robot to behave more like a human being to achieve those tasks," said postdoctoral research fellow Stephen McKinley.

Source: Interesting Engineering

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