You may not realize this, but you interact with AI technology on a consistent, if not daily basis. And if you do recognize it, chances are good that you take it for granted. Whether it’s a Spotify playlist, an Alexa reply, or one of the myriad cool things Google Assistant does, it’s powered by AI and cloud technology.

More and more, companies are turning to cloud technology for AI applications, and that means the demand for developers and architects is steadily rising.

The Google Cloud Platform, one of the largest in the space, is a suite of computing services and tools that power Google’s Search, YouTube, and much more. According to Glassdoor, a GCP Cloud Architect can pull in a starting salary of $120,000- $160,000. Ready for a piece of that?

Google Cloud computing isn’t exactly something you master overnight. Hell, it could take you weeks just to form a basic understanding of it. It takes time to learn topics like deploying and implementing cloud solutions, software-defined networking, or virtual private clouds.

Fortunately, you can kick-start your education with some online training. Take the Google Cloud Certifications Practice Tests + Courses Bundle, for instance. This comprehensive online training features 43 hours of lectures and other tools to help prepare you for a career in the emerging field.

Sign up, and you’ll get lifetime access to the training so feel free to really dig in and learn things. Or, if you’re like many of us, drop in and out and spend the rest of the pandemic period fine-tuning yourself.

Considering how incredibly valuable the information in this 7-course bundle is, $29.99 is a small price to pay. It’s worth more than $630 if you were to purchase yourself, but we’d never let you pay that much.

Save even more!

In addition to the savings above, when you buy through AndroidGuys Deals, for every $25 spent, you get $1 credit added to your account. What’s more, should you refer the deal via social media or an email that results in a purchase, you’ll earn $10 credit in your account.

The post Unlock a new career in Google Cloud with this mastery bundle appeared first on Artificial Intelligence.

DataRobot, the leader in enterprise AI, announced enhancements to its enterprise AI platform, including AI Applications, Automated Deep Learning, and Visual AI. These new introductions further unlock the value of AI by putting the power of AI into the hands of more users and making it simpler to build and deploy deep learning models.

“Subject matter experts from any industry can now solve new business problems by including relevant image-based content along with other more traditional data types. This latest evolution of our platform will empower users to leverage AI to make even better decisions based on broader perspectives.”

In the latest version of the platform, DataRobot has introduced:

• Visual AI: With Visual AI, users can address computer vision use cases and combine incredibly diverse types of data in their models. Visual AI offers immediate support for use cases requiring image recognition and classification. Users can simply drag and drop a collection of images into a project and build custom deep learning models in minutes. DataRobot’s Visual AI then takes image-based machine learning one step further by allowing users to leverage images alongside any other feature types such as numeric, categorical, dates, and raw text.
• AI Applications: With the latest platform release, any machine learning model, including DataRobot-generated models or models written in R or Python, can be turned into an AI application. This enables employees of all skill levels to interact with the predictive insight of the underlying model and experiment with different scenarios, predict results, and make more informed business decisions. The new feature also includes an Applications Gallery – a one-stop shop that allows business users to find the application that best suits their needs.
• Automated Deep Learning: DataRobot has significantly boosted its deep learning capabilities, powered by a new Keras-based model framework for which DataRobot recently secured a provisional patent. Traditionally, training deep learning models is expensive and time consuming. DataRobot’s new capabilities allow users to build successful and reliable deep learning models that are ready to deploy into production. The new capabilities also make it easy to understand these models – all with the infrastructure a user has in place.

“Having pioneered the automated machine learning category, we are proud to push the boundaries of what’s possible with the technology by offering these novel automated deep learning and Visual AI capabilities,” said Phil Gurbacki, SVP of Product and Customer Experience, DataRobot. “Subject matter experts from any industry can now solve new business problems by including relevant image-based content along with other more traditional data types. This latest evolution of our platform will empower users to leverage AI to make even better decisions based on broader perspectives.”

Additionally, DataRobot has unveiled enhancements to:

• MLOps: In this release, DataRobot MLOps has been enhanced to include pre-packaged model environments so users can drag-and-drop model files, developed in languages such as Python and R, and deploy them using Kubernetes. The release also includes unlimited batch scoring with integrations to leading cloud storage options for massive scale. Lastly, the enhanced MLOps solution offers Monitoring Agents that can capture metrics from models deployed to almost any environment.
• Automated Time Series: Automated Time Series now features new deep learning techniques that remove the traditional forecasting barriers to make easy work of large-scale multi-series forecasting applications.
• DataRobot Paxata: Following the acquisition of Paxata in December 2019, DataRobot has integrated Paxata’s AI-assisted data preparation solution seamlessly with its AI Catalog to empower novice and expert users to rapidly explore, clean, combine, and shape data for training and deploying machine learning models.

The post DataRobot Unveils Latest Version of Enterprise AI Platform, Introducing Visual AI, AI Applications, and Automated Deep Learning appeared first on Artificial Intelligence.

Artificial intelligence has revolutionised every piece of technology it has touched. However, this augmentation — for better or worse — has also brought up a lot of confusion. With more and more AI application coming up in different fields, specifically in automation like Cognitive Automation, the conditions associated with it give the impression that the technology is artificially intelligent and seems to dilute the real meaning behind it. This poses a more significant problem as what qualifies as a mere application of AI can be called artificial intelligence.

When we talk about automation and AI, there is a lot of buzz around cognitive automation as it uses technology to mimic human behaviour and precisely the reason why some people call it as cognitive automation artificial intelligence.

Artificial Intelligence Vs Cognitive Automation

If one had to define artificial intelligence regarding computing, then it can be defined as the area of computer science that focuses on the creating intelligent machines that work and interact like humans with each other or with living beings. Some activities include speech recognition, learning, among others. When it comes to AI’ creating intelligent machines that work like humans’ is what one has to keep in mind from the definition. The creation process depicts the intelligence part of the device.

For example, AI in healthcare has had many applications over the years. Now, if a doctor wants to take the help of an AI, then during a particular procedure, intelligence comes into play when AI suggests which course of action to choose based on its analysis.

Intelligence, especially artificial intelligence, requires a lot of information to carry out its analysis about a process.

On the other hand, cognitive automation mimics quantitative human judgement or augments human intelligence. In short, cognitive automation imitates human thinking. If you look at the technologies in cognitive automation — like natural language processing, image processing and contextual analysis — all are more profound concepts of perceptions and judgements and are heavily influenced by AI.

If one looks at the cognitive applications, it becomes evident that the automation happens via hardcoded human-generated rules or through dense inputs.

According to François Chollet, creator of the neural network library, Keras, “Automation is, at best, robustly handling known unknowns over known tasks, which is already incredibly difficult and resource-intensive in the real world — whether engineering or data.”

Therefore, when it comes to automation, it can only work if it is made aware of the unknowns. Working with the unknown entirely on itself will only result in the failure when it comes to automation. For instance, in the healthcare sector, doctors do take the help of AI for deciding the course of action based on the suggestions made by the intelligent system. However, when it comes to automation, this technology is only here to enhance the doctor’s practice and not independently run any analysis.

Cognitive automation learns through different unstructured data and connects to creating tags, annotations and other metadata. Cognitive automation tries to find similarities between items to specific processes. It seeks to identify the mentioned items in the process and then searches for similar ones in order to connect them.

Why The Confusion?

To carry out a process by an automation system requires data. And, once enough information has been provided during the automation process, there is no requirement for humans to build an additional model to carry out the analysis further. As the new data set is provided, the automation makes more connections with the old one, which allows the cognitive automation systems to keep learning without any supervision and can continuously adjust to the new information.

Whereas for AI — it carries out its analysis after been given a different data set at the expense of a massive amount of information which has been fed to the system. This information/data is more than the required data for cognitive automation.

Outlook

In the current scenario, when one reads about the cognitive applications, the process and its workings might be similar to artificial intelligence, and thus creating confusion between the two. This happens because ultimately, cognitive automation is an application of artificial intelligence itself, which is just a little less intelligent. Cognitive automation doesn’t deal with the unknowns of a process or the real-world problems, and it can only work through them if there is data fed to it in.

The post IT’S 2020 — STOP CONFUSING COGNITIVE AUTOMATION WITH ARTIFICIAL INTELLIGENCE appeared first on Artificial Intelligence.

The post 6 Design Principles for Artificial Intelligence in Digital Business appeared first on Artificial Intelligence.

Artificial intelligence (AI) is becoming ever more powerful. Consulting firm PwC estimates that AI could contribute up to $15.7 trillion to the global economy in 2030, more than the combined GDP of China and India today. The technology will soon be omnipresent—from household appliances to our financial, law and justice systems.

That is why we should be very worried about the dark side of AI. And this is not about devilishly powerful AIs making humans slaves, as depicted in science fiction. The danger is much subtler.

AI will help us make decisions, and in many cases, take decisions for us, but AI is only as good as the data that is fed into it. The data is worked on by deep-learning software, which absorbs the data, figures out patterns, creates rules to fit the patterns, and keeps tweaking those rules as more data is fed into it.

In many cases, the programmers themselves are unaware of how the AI reaches decisions. The workings are so complex that they become opaque.

These masses of data are fed into the system by humans. And as humans, we all carry prejudices, consciously or unconsciously. This may colour the data we give the AI to crunch. Also, all the data that is fed is current or historical. It will reflect societal biases.

For instance, if an AI is fed the resumes of candidates for a top corporate job, it is almost certain that the system will choose a man, because data shows that men have overwhelmingly outnumbered women as CEOs in the past.

Going by the data, the AI will decide that a man will make a better CEO than a woman.

This data may have nothing to do with the talents or competence of women managers but the fact that they were not promoted due to gender bias. But the AI will never know that. It does not and cannot have any concept of “fairness”. It only knows data. And the idea of what is fair differs from society to society.

The notions also change over time as societies evolve. But the AI will never know that.

Inherently biased data can affect credit ratings, insurance plans, one’s higher education and career. In fact, it can change one’s life.

In 2016, an investigation by American non-profit organisation ProPublica found that COMPAS, an AI-driven software that assesses the risk of a person committing a crime again, was biased against people of colour. But judges in some US states are still using the software.

In 2015, Google had to apologize after its photo app tagged two black people as gorillas—perhaps because the algorithm’s training dataset did not have pictures of enough black people.

In 2016, Russian scientists ran a global beauty contest to be judged by an AI. Of the 44 winners, only one had dark skin. The algorithm had been trained mostly with photos of white people, and it had equated “fair skin” with “beauty”.

A study of Google’s AI-driven advertising platform found that men were shown ads for high-paying jobs more often than women. Same with LinkedIn’s job ads.

AIs can also polarize society. On social media networks, deep-learning algorithms make sure that users are shown content that conforms to their preferences and biases. This creates a “filter bubble”.

I keep seeing opinions that resonate with mine, however loony they are, and over time, this makes me more isolated from and less tolerant towards opposing views. Social and political divides are deepened. This is how the Russian hackers cracked the 2016 US presidential elections.

And the more you use a biased AI, more biased data is created that the algorithms will use: a perfect feedback loop of insidious bias.

What if governments start using AI to take decisions on matters like resource allocation and national security? Politicians may lose power, or retire, but the AI (by now opaque in its complexity) will keep spewing out results, even though they may have calamitous consequences in the real world.

Mere technologists will never be able to solve the problem. More than technical, these are human, ethical and philosophical issues.

Some serious questions need to be answered before we jump, whistling and cheering, on the AI bandwagon.

The post Opinion | Beware the dark side of Artificial Intelligence appeared first on Artificial Intelligence.

NetApp, the data authority for the hybrid cloud, today introduced NetApp® ONTAP® AI proven architecture, powered by NVIDIA DGX supercomputers and NetApp AFF A800 cloud-connected all-flash storage to simplify, accelerate, and scale the data pipeline across edge, core and cloud for deep learning deployments and to help customers achieve real business impact with AI.

“Companies of all sizes and across various industries are making significant inroads in using AI to solve real-life problems, especially in the enterprise. However, while very exciting, AI models and workloads are not easy to deploy, and many organizations are struggling,” said Ritu Jyoti, program vice president, IDC. “The NetApp ONTAP AI proven architecture is a powerful solution that will help users accelerate AI-driven business outcomes and overcome deployment obstacles.”

“Developing disruptive AI technology and turning this into breakthrough products and services for our customers is a vital requirement across many markets we work in,” said Monty Barlow, head of artificial intelligence, Cambridge Consultants. “NetApp ONTAP AI powered by NVIDIA DGX supercomputers and NetApp all-flash storage is simplifying and accelerating the data pipeline for deep learning.”

“Organizations everywhere are making significant investments in AI, but they often lack the infrastructure required to optimize their data for AI projects and scale their applications and workflows to create the business impact they expect,” said Octavian Tanase, senior vice president, ONTAP, NetApp. “NetApp’s cloud-connected data solutions and new proven architecture with NVIDIA DGX create a single data environment for AI. This gives customers the control, access, and performance they need to provide the right data at the right time at the right location to their AI applications – all at scale and all integrated, managed, and protected by the NetApp Data Fabric.”

By leveraging the NetApp Data Fabric, ONTAP AI enables enterprises to create a seamless data pipeline that spans from the edge to the core to the cloud. This pipeline integrates diverse, dynamic, and distributed data sources, with complete control and protection. With massive processing power and capacity, ONTAP AI removes performance bottlenecks and enables secure, nondisruptive access to data from multiple sources and data formats.

“The combination of NVIDIA DGX and NetApp all-flash arrays meets the infrastructure challenges of today’s AI deployments,” said Jim McHugh, vice president and general manager of Deep Learning Systems at NVIDIA. “NVIDIA’s leadership in AI and GPU computing, combined with NetApp’s innovation in all-flash storage systems, gives customers a faster way to deploy AI with proven performance and a simplified operations model.”

ONTAP AI is a joint NetApp and NVIDIA proven architecture offering that combines the world’s most powerful GPU solutions with the world’s fastest and most cloud-connected flash systems and software.

Features and benefits include:

• Deploy easily. Get started faster with AI by eliminating design complexity and guesswork.
• Scale without limits. Organizations that are starting out with deep learning can begin with a 1:1 configuration and scale out as their data grows, to a 1:5 configuration and beyond.
• Operate with confidence. High-availability solution design with redundant storage and network and server connections.

An Ecosystem of Solution Partners

ONTAP AI is available through channel partners, including:

“As long as we have partnered with NetApp, their products have been able to adapt to address the challenges around data that come with major technological advances. AI is a key component to building modern applications that are engaging, real time, and distributed. NetApp ONTAP AI and Data Fabric make it easier to leverage data wherever it is, to build and deploy powerful AI-powered applications,” said Mark Kelly, chief strategy officer at ePlus. “ONTAP AI simplifies ongoing operations and accelerates time to results. With our AI expertise and track record in converged infrastructure, we can help customers focus less on infrastructure and more on driving outcomes from their deep learning environments.”

“Customers want an AI solution that allows them to start small and grow. ONTAP AI provides a proven architecture to help them achieve that goal,” said Manohar Vellala, practice manager, Data and Analytics at Groupware Technology. “Our customers can now deploy a powerful, highly available deep learning configuration validated by NetApp and NVIDIA with the headroom to grow with their needs.”

“IAS is excited to be a launch partner for the ONTAP AI proven architecture,” said Amy Rao, CEO of IAS. “Moving large datasets from edge to core or core to cloud is becoming impractical, and customers are looking for ways to maximize data value independent of location. The Data Fabric—combined with NetApp cloud-connected all-flash technology and NVIDIA GPU-assisted compute and all packaged in a prevalidated configuration—gives customers the choice, control, efficiency, and availability required for deep learning environments.”

“Customers deploying AI and deep learning are looking for ways to reduce their time to outcomes. A highly available and powerful deep learning environment ensures that data scientists stay productive,” said Bob Olwig, vice president of Corporate Business Development, WWT. “ONTAP AI is a proven architecture from NetApp and NVIDIA that complements WWT’s broad portfolio of solutions and services for deep learning, giving customers an AI stack that can be implemented quickly with higher efficiency and lower risk.”

The post NetApp and Nvidia Supercharge Deep Learning With New AI Architecture appeared first on Artificial Intelligence.

Does all the hype about AI sound just a little too familiar? If you’re old enough to remember the first beginnings of the Internet and the dotcom bubble, you might also remember the tsunami of hype that attended these events as they unfolded. Wiredmagazine made endlessly breathless predictions about how the Internet would transform humanity and bring about a technologically-driven utopia. Now we’re wrestling with how such a promising technology devolved into a netherworld of hacking, hate speech, exploitation of personal data, “dark webs”, misinformation, political chicanery, and citizen surveillance despite these glowing promises. In the latest twist, AI is being sold in a similar way by similar players and the cultural amnesia is impressive.

In the early nineties, I had the unusual experience of being the first journalist to write about the advent of the Internet. The genesis was that Vinton Cerf, “the father of the Internet”, was on the advisory board for a technology magazine where I was a staff editor. Vint had sent me an email describing how a Department of Defense network would soon be commercialized and available for public use. We ran an article breaking the news about this exciting development—the birth of the public Internet. The articles ran three months before the New York Times broke the story. It was from this vantage point that I watched the Internet hype bubble expand for almost a decade—not just in technology publications but in the mainstream media as well.

The Technology Hype Machine

What’s past is prologue. The non-stop torrent of hype about AI is now taking us down the same path but with an important difference. While the Internet did, in fact, provide democratizing benefits by empowering users and still continues to do so, AI is a far more exotic and inaccessible technology. As such, it will be developed and controlled by well-funded and powerful organizations, whether the same Silicon Valley giants that have already been exposed as trampling over the rights of their users or corporations that will use it to exert more repressive control over employees.

As an editorial in The Economist gushed: “Using AI, managers can gain extraordinary control over their employees. Amazon has patented a wristband that tracks the hand movements of warehouse workers and uses vibrations to nudge them into being more efficient. Workday, a software firm, crunches around 60 factors to predict which employees will leave. Humanyze, a startup, sells smart ID badges that can track employees around the office and reveal how well they interact with colleagues.” Is the workplace of the future sounding like fun yet?

Psychopathic AI?

Giving corporations powerful and manipulative technology tools to intrude even more deeply into the personal lives of consumers and employees and then analyze that data for financial gain is a dangerous combination and a blueprint for dystopia. As was the case with the Internet, I believe that AI will end up providing some important benefits if and when used ethically and thoughtfully. But it also has the potential to do a lot of harm. Witness, for example, the “psychopathic AI” being developed by MIT students.

My sense as a futurist, however, is that there’s also some good news here: Internet users are becoming wiser and more guarded about the unintended consequences of technologies that lure us in but then exact a steep price. The first wave of a user revolt came in the vigorous pushback that monopoly-provider Facebook received after its egregious privacy transgressions were revealed. I believe that over time, and as they come to better understand the implications, people will begin to reject AI applications and programs that are offensive, exploitative, and dehumanizing. In this sense, corporatized AI will eventually fail as some kind of utopian platform for the common good, just as the Internet in many cases has become a tool for subtly institutionalizing social and economic advantage. Here are some observations about how and why I think this will happen:

AI will create widespread dehumanization and depersonalization. The rise of computing in everyday life has already created a “mediated society” as I described in Digital Mythologies. Here in the US, citizens often find themselves relating to each other not directly but through some sort of mediated digital experience. Over time, these dehumanizing experiences can chip away at our sense of humanity and have profound cultural effects. However,  many are now waking up to the Faustian bargain of having an AI-precursor technology such as Alexa cater to our every whim, on the one hand, while robbing us of privacy and personal freedom on the other and making us fully dependent on hyper-technology for the essentials of life.

In my opinion, a fully implemented AI world is unlikely to happen because humans now have a powerful desire to reconnect with the natural world, undo the massive harm that’s been done to the planet in the name of progress, and reclaim their independence from the subtle chokehold, de facto dependency, and limited range of personal choice that characterizes a mechanistic, technology-driven lifestyle. Technology is here to stay but only those systems, products, and services that don’t impede this goal will ultimately succeed.

AI will make people dumber and more dependent on “the system”. When calculators first came out, students who used them eventually began to forget how to add subtract and divide. Their skills simply atrophied. Now that the use of Google search is widespread, it’s been observed that Internet users no longer feel it necessary to remember either history and or basic facts because they can be so easily looked up. Some observers think that this is making us less intelligent and changing the patterns of how we think. In his book The Shallows: What the Internet Is Doing to Our Brains, Nicholas Carr observed: “The redirection of our mental resources, from reading words to making judgments, may be imperceptible… but it’s been shown to impede comprehension and retention, particularly when repeated frequently.”

There are other points to consider. Many of us no longer use maps because GPS makes them unnecessary. But without a map you no longer have a cognitive sense of your own surroundings. There’s no need to pay attention to where you are physically. This notion of physically is incredibly important. In a very subtle way, a person gives up a little of their independence of thought to a machine and becomes a less skillful negotiator of life’s terrain, both literally and figuratively. That aspect of life becomes an abstraction, a fog of unknowing. In losing touch with your surroundings, you also lose touch with the natural world, already a hazard of living in an advanced Western nation.

I would argue that not only is technology changing society and culture for the worse in some areas of life, but that this shift is not just cultural but ontological. AI and the overuse or inappropriate use of computer technology has the potential to remove us from the natural world and to literally abstract as well as distract us from our surroundings. This can include people, places, and the fullness of many life experiences (Think of a traveler glued to their smartphone while strolling through the streets of Paris.) In making this existential shift, AI will also make humans more dependent on their systems, less self-reliant, less intelligent, and less educated in the traditional sense of education as a way of apprehending and appreciating the world at large. Intelligence will be valued, but primarily in machines, not humans and in those who develop and own the most capable machines who will become the new “masters of the universe”. Wisdom, as we now know it, will be diminished because it is not a programmable attribute.

AI will increasingly be used as means of social control. AI is very sophisticated and powerful technology. Following the pattern of social Darwinism that has characterized much of the Silicon Valley’s initiatives to date, it confers power on those who direct its aims. This makes it a very different animal than the Internet which, for all its flaws, still manages to empower individuals. Who are the powerful entities that will end up controlling and dispensing AI? No surprise—corporations that can afford to buy and perfect this technology and also governments using it to track the lives and movements of citizens.

Authoritarian countries such as China will be the first to perfect this. In China, AI is already being used to implement a social acceptability score that ranks the “value” of its citizens to the government and determines to what extent the citizen is eligible for government-provided benefits.

As described in an article in Fast Company: “By 2020, the country plans to give all its 1.4. billion citizens a personal score, based on how they behave…The government started working on its so-called social credit system back in 2014, which ranks citizens on their trustworthiness, including whether they jaywalk, buy Chinese-made products, what they post online, and whether they smoke in nonsmoking areas. Those deemed trustworthy can get discounts on energy bills and better interest rates at banks, while those considered untrustworthy can reportedly be stopped from buying property and even high-speed internet.”

While there may be no short term cure for how AI is being applied in authoritarian regimes, the most optimistic perspective is that dystopian uses of AI will eventually be resisted in Western democracies. The hope is that this will happen as citizens become increasingly less starry-eyed and more well informed about who really benefits from these technologies and how, and over the course of time, they have the potential to greatly diminish the quality of life.

The post Is Artificial Intelligence Too Dehumanizing to Succeed? appeared first on Artificial Intelligence.

By Will Yang

Technology in today’s day and age has enabled a human to do things and accomplish far more than one could think of a few years back. Thanks to rapidly evolving and innovative technologies, personal lives have become more enriched. Meaningful collaborations between a human and machine/technology has in many ways provided a wealth of opportunities to us making our lives comfortable. One such technology buzzword in the industry today is Artificial Intelligence. Once a topic for science fiction, Artificial Intelligence technology is now being used by brands across industries and categories. Artificial Intelligence is essentially the creation of systems that use advance analytic strategy, especially machine learning and deep learning to accomplish things that previous we thought only people could do. Thus, brands are now leveraging the technology and innovating constantly to offer efficient solutions to everyday problems – from reminders, personalized experiences to tailored medical treatments, Artificial Intelligence promises to transform the way we live by smartly assisting us.

Artificial Intelligence Applications in Everyday Life

Every human today in one way or other is embracing Artificial Intelligence, some with relevant knowledge and understanding of the concept and others out of habit. Popular applications of Artificial Intelligence in everyday use include Smart Personal Assistants like Siri by Apple, Google Assistant and Amazon’s Alexa. These assistants use Artificial Intelligence as they collect information on an individual’s requests and use that information to better recognize our speech and serve us results that are tailored to our preferences. Video games also use Artificial Intelligence to enhance consumer experience where the characters come alive by learning the gamer’s behaviours, respond to stimuli and react in unpredictable ways. Another great example is the widely discussed and trending topic – smart cars. While it still has a long way to go, Artificial Intelligence will be able to enable a car look ahead of it and make decisions as it goes and keep improving and learning the process. Even social media apps like Facebook use Artificial Intelligence to recognize faces and personalize your news feed. If you notice, you consume the most of this technology on your smartphone. This is because it’s your constant. Wherever you go, you carry it, it’s like an extension of your personality. Artificial Intelligence has become an important aspect towards transforming the modern smartphones, soon our handheld devices will be referred to as ‘Intelligent phones’.

New Chapter in Smartphone Photography

One of the most advanced innovations using Artificial Intelligence has been done by smartphone brands in the world of photography. Many brands are offering Artificial Intelligence powered cameras that has completely redefined smartphone photography, to an extent that photographers are using these advanced phone cameras to capture images instead of DSLRs. It can be easily said that brands are investing in this sphere and using Artificial Intelligence to enable the phone’s camera interface to detect the subject in the camera frame and accordingly adjust the settings for the best possible image.

Artificial Intelligence Creating Ripples in the Front Camera Segment

With the love of selfie snapping becoming an increasing global trend and the rage taking over millions where people are uploading their selfies all over their social media accounts, selfie or front cameras have become equally important. Earlier consumers based their buying decision on the rear camera but in recent times the focus has shifted to the front camera. Realizing this trend, brands are now investing in front camera technologies that make user’s lives more beautiful via the front camera experience. Artificial Intelligence can identify facial features and automatically enhance them for a superior portrait. It does so by customizing or offering personalization for the user or subjects within an image. The smart technology enables the camera to recognize skin tone and type, gender and age of all subjects in the image, environment lighting, while referencing hundreds and thousands of global user photo database and learn from past selections, to optimize each selfie shot.

What’s Up Ahead?

As per a 2017 Counterpoint Research, one in three smartphones, roughly more than half a billion, shipped in 2020 will come with chipset-level integration with machine learning and Artificial Intelligence. Thus, we can definitely say that the smartphone’s future is all about the camera. We saw a lot of developments in the camera technology in 2017, but 2018 could be the year where Artificial Intelligence comes into play strongly, completely redefining the way we one uses their phone cameras and a lot more from writing emails to distant relatives to having awkward conversations with Siri or Alexa. The era of Artificial intelligence is driven by machine learning, extreme automation and omnipresent connectivity. The near future will only see a rise in the usage of AI tech and advancements to perfection.

The post Artificial Intelligence: Redefining photography in the smartphone world appeared first on Artificial Intelligence.

Most of us approach healthcare with hesitation. There are often several deterrents to seeking medical care (let alone preventive care)—ease of access being a major one. According to the Indian Journal of Public Health(September 2017 edition), India had just 4.8 practicing doctors per 10,000 population. While this is expected to grow to 6.9 doctors per 10,000 people by 2030, the minimum doctor-to-patient ratio recommended by the World Health Organization (WHO) is 1:1000.

Can we have more doctors? That is easier said than done. Even if we can, with our education system focused on quantity over quality, churning out doctors in droves will not guarantee better quality medical services in our country. Besides, with most graduates preferring lucrative urban locations, many Indians still find themselves at great physical or economic distance from quality healthcare.

Essentially, what we need is to fill the gap between the needs of the plenty and the services of the few. In my view, artificial intelligence (AI) has the capability to enable solutions that form the critical middle layer of access—making healthcare accessible and affordable to a large population base at the same quality level irrespective of people’s social standing.

Let us see how AI is set to impact our health in the coming years.

Reactive versus proactive healthcare

The usual attitude towards healthcare is, “I’ll cross that bridge when I come to it”—a reactive rather than proactive approach to seeking medical intervention. However, that has started changing in recent times. Sensors in wearables such as smart watches and Fitbits are already equipped to deliver actionable feedback to apps in our phones and connect to our doctor’s clinic for diagnostic tests and medication prescriptions.

Today, for instance, Apple watches can detect a variety of heart diseases—including diabetes prediction with an 85% match in known cases. All this by using simple, non-invasive tech that is already available.

Interesting AI start-ups like SigTuple (digitized blood analysis), Niramai (thermal scans for breast cancer) and Ten3T (portable, easy-to-use electrocardiograms) are currently developing disruptive diagnostic solutions that will significantly bring down costs, while making physical distance a non-issue. They (and others in this area) do this via cloud-based linkages to hospitals and clinics, chatbots, smart apps and AI-enabled data analytics. This means that sensors, real-time tracking and analytics will enable us to take pre-emptive charge of our own health, helping us to live more aware, healthier, longer lives.

Eliminating human biases

Barring simple ailments, most health consultations and treatments today come with some human bias. Sometimes, there are doubts if doctors’ or pharma companies’ vested interests are pushing certain treatments and medicines. That is why we gravitate towards known doctors. And for serious illnesses and critical care, second opinions are always recommended. In this context, AI-enabled medical care can save time, effort and costs through easy access to unbiased, consistent, good-quality diagnosis and treatment.

The experience of doctors also determines the options they explore. AI makes it possible to access the learnings and data from hundreds of thousands of cases. Oncology, for example, is an area where doctors are continually combining and recombining drugs to treat cancers or overcome resistance to previously successful drugs. Already, AI algorithms are helping doctors analyse a much wider scope of data and predicting—with greater granularity—new drug combinations that are personalized for a patient’s specific need.

Democratizing healthcare for 1.3 billion

Since technology can provide the middle layer bridge, AI-led systems have the potential to take healthcare to people irrespective of their location and affordability. People who live in rural or far-flung locations no longer have to be deprived of the up-to-date care offered at the nerve-centres of medical research. The tech increases the accessibility manifold. We thus have an opportunity to make this world truly equal.

Healthcare today—not just in India but pretty much the world over—is riddled with inefficiencies, and it’s easy to complain about them. However, we are poised for change, and technology has the potential to bridge the chasm between quality care and the many that need it but currently can’t access or afford that care. The beginnings have been made, and the work is well underway—significantly so in India—towards creating a level-field in healthcare.

The post Artificial Intelligence can make healthcare more accessible, affordable appeared first on Artificial Intelligence.

The post Artificial Intelligence lifeline for India’s flailing healthcare appeared first on Artificial Intelligence.