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Deep Learning to Boost Ghost Hunting and Paleontology Efforts

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Deep Learning to Boost Ghost Hunting and Paleontology Efforts

Deep leaning technology is taking the world by storm. It is leaving no territory untouched, not even the world of dead! Yes, this robust technology has now started hunting ghosts – for real. Of late, Nature Communication even published a paper highlighting that a ghost population has even contributed to today’s genomes.

With the help of a demographic model structured on deep learning in an Approximate Bayesian Computation framework, it is now possible to delve into the evolutionary history of the Eurasian population in sync with the present-day genetic evidence. Since it is believed that all modern humans have originated Out of Africa, the evolutionary history of the Eurasian population has been identified by introgressions from currently extinct hominins. What’s more, talking about the unknown population, the researchers believe they either trace their roots to Neanderthal-Denisova clade or simply forked early from the Denisova lineage.

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If you want to take a look at the original paper, click here — www.nature.com/articles/s41467-018-08089-7

In addition, the study reflects how the fabulous technology of AI can be leveraged in paleontology. Whether it’s about discovering unpredictable ghosts or unraveling the fading footprints of the whole evolutionary journey, deep learning and AI are taking the bull (paleontology, in this respect) by its horns. According to the paper, researchers studied deep about the evolutionary process of Eurasian population, including past introgression events in OOA (Out of Africa) populations suiting the contemporary genetic evidence and they have produced several simulated evolutionary data, like the total size of ancestral human populations, the exact number of populations, the appropriate time when they branched out from one another, the rate at which they intermixed and so on. Besides, a wide number of simulated genomes for current-day humans have been launched.

The latest and very efficient deep learning method highlights the crucial importance of genomes – they can easily let you know which evolutionary models are most likely to reveal respective genetic patterns. Moreover, if you study closely, you will find that the culture of the entire industry has changed over the past few years. Advanced computers and technology modifications have achieved ‘things’ that were simply impossible with pen and paper a few years back. Perhaps, what’s more interesting is that our perspective of seeing data has changed completely. The potent advances in AI and machine learning have demystified the ways in which algorithms work leading to more concrete shreds of evidence and end-results, which were previously not possible with the age-old traditional methods.

The blog first appeared on— www.analyticsindiamag.com/deep-learning-uncovers-ghosts-in-modern-dna

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AI Jobs: What the Future Holds?

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AI Jobs: What the Future Holds?

Technological revolutions have always been challenging, especially how they influence and impact working landscapes. They either bring on an unforeseen crisis or prove a boon; however, fortunately, the latter has always been the case, starting from the innovation of steam engines to Turing machine to computers and now machine learning and artificial intelligence.

The crux of the matter lies in persistence, perseverance and patience, needed to make these high-end technologies work in the desired way and transform the resources into meaningful insights tapping the unrealized opportunities. Talking of which, we are here to discuss the growth and expansion of AI-related job scopes in the workplace, which is expected to generate around 58 million new jobs in the next couple of years. Are you ready?

Data Analysts

Internet of Things, Machine Learning, Data Analytics and Image Analysis are the IT technologies of 2019. An exponential increase in the use of these technologies is to be expected. Humongous volumes of data are going to be leveraged in the next few years, but for that, superior handling and management skill is a pre-requisite. Only expert consultants adept at hoarding, interpreting and examining data in a meaningful manner can strategically fulfill business goals and enhance productivity.

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IT Trainers

With automation and machine learning becoming mainstream, there is going to be a significant rise in the number of IT Trainer jobs. Businesses have to appoint these professionals for the purpose of two-way training, including human intelligence as well as machines. On one side, they will have to train AI devices to grasp a better understanding of human minds, while, on the other hand, the objective will be training employees so as to utilize the power of AI effectively subject to their job responsibilities and subject profiles. Likewise, there is going to be a gleaming need for machine learning developers and AI researchers who are equipped to instill human-like intelligence and intuition into the machines – making them more efficient, more powerful.

Man-Machine Coordinators

Agreed or not, the interaction between automated bots and human brainpower will lead to immense chaos – if not managed properly. Organizations have great hope in this man-machine partnership, and to ensure they work in sync with each other, business will seek experts, who can devise incredible roadmaps to tap newbie opportunities. The objective of this job profile is to design and manage an interaction system through which machines and humans can mutually collaborate and communicate their abilities and intentions.

Data Science Machine Learning Certification

Security Analysts

Security is crucial. The moment the world switched from offline to online, a whole lot of new set of crimes and frauds came into notice. To protect and safeguard confidential information and high-profile business identities, companies are appointing skilled professionals who are well-trained in tracking, protecting and recovering AI systems and devices from malicious cyber intrusions and attacks. Thus, skill and expertise in information security, networking and guaranteeing privacy is well-appreciated.

No wonder, a good number of jobs are going to dissolve with AI, but also, an ocean of new job opportunities will flow in with time. You just have to hone your skills and for that, we have artificial intelligence certification in Delhi NCR. In situations like this, these kinds of in-demand skill-training courses are your best bet.

 

The blog has been sourced from ―  www.financialexpress.com/industry/technology/artificial-intelligence-are-you-ready-for-ocean-of-new-jobs-as-many-old-ones-will-vanish/1483437

 


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More than Statistics, Machine Learning Needs Semantics: Explained

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More than Statistics, Machine Learning Needs Semantics: Explained

Of late, machines have achieved somewhat human-like intelligence and accuracy. The deep learning revolution has ushered us into a new era of machine learning tools and systems that perfectly identifies the patterns and predicts future outcomes better than human domain experts. Yet, there exists a critical distinction between man and machines. The difference lies in the way we reason – we, humans like to reason through advanced semantic abstractions, while machines blindly depend on statistics.

The learning process of human beings is intense and in-depth. We prefer to connect the patterns we identify to high order semantic abstractions and our adequate knowledge base helps us evaluate the reason behind such patterns and determine the ones that are most likely to represent our actionable insights.

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On the other hand, machines blindly look for powerful signals in a pool of data. Lacking any background knowledge or real-life experiences, deep learning algorithms fail to distinguish between relevant and specious indicators. In fact, they purely encode the challenges according to statistics, instead of applying semantics.

This is why diverse data training is high on significance. It makes sure the machines witness an array of counterexamples so that the specious patterns get automatically cancelled out. Also, segmenting images into objects and practicing recognition at the object level is the order of the day. But of course, current deep learning systems are too easy to fool and exceedingly brittle, despite being powerful and highly efficient. They are always on a lookout for correlations in data instead of finding meaning.

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How to Fix?

The best way is to design powerful machine learning systems that can tersely describe the patterns they examine so that a human domain expert can later review them and cast their approval for each pattern. This kind of approach would enhance the efficiency of pattern recognition of the machines. The substantial knowledge of humans coupled with the power of machines is a game changer.

Conversely, one of the key reasons that made machine learning so fetching as compared to human intelligence is its quaint ability to identify a range of weird patterns that would look spurious to human beings but which are actually genuine signals worth considering. This holds true especially in theory-driven domains, such as population-scale human behavior where observational data is very less or mostly unavailable. In situations like this, having humans analyze the patterns put together by machines would be of no use.

End Notes

As closing thoughts, we would like to share that machine learning initiated a renaissance in which deep learning technologies have tapped into unconventional tasks like computer vision and leveraged superhuman precision in an increasing number of fields. And surely we are happy about this.

However, on a wider scale, we have to accept the brittleness of the technology in question. The main problem of today’s machine learning algorithms is that they merely learn the statistical patterns within data without putting brains into them. Once, deep learning solutions start stressing on semantics rather than statistics and incorporate external background knowledge to boost decision making – we can finally chop off the failures of the present generation AI.

Artificial Intelligence is the new kid on the block. Get enrolled in an artificial intelligence course in Delhi and kickstart a career of dreams! For help, reach us at DexLab Analytics.

 

The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2019/01/15/why-machine-learning-needs-semantics-not-just-statistics/#789ffe277b5c

 

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Deep Learning: Is It Still on the ‘Hype Cycle’?

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Deep Learning: Is It Still on the ‘Hype Cycle’?

Interestingly, the last decade has witnessed some phenomenal leaps in the technology domain, notably in AI. As compared to the early days of speech recognition, smartphones we use today have transformed themselves entirely; they are more like our virtual assistants: the reason being quantum advancements in Deep Learning and Machine Learning.

The craze surrounding Deep Learning continues to grow. In this blog, we will evaluate whether the trend is going to stay for long and influence the future of AI or is it just a hype which will soon disappear into thin air.

The Hype Cycle

In simple terms, a ‘hype cycle’ refers to a curve that escalates to a peak at the start, then drops sharply and gets into a plateau. Perhaps not surprisingly, Deep Learning has been a part of diverse ‘hype cycles’. Currently, if you follow the tech market statistics, you will find that DL is yet to reach the plateau of productivity, where it would be largely accepted by the public and leveraged for daily work. As of now, DL hasn’t reached that stage, that’s why we can’t confirm whether the technology is going to stay or dwindle away.

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From a DL Enthusiast’s Perspective

Following present-day market trends, we can say that virtual reality and augmented reality are close to the plateau of productivity. Years back, when these advanced technologies were launched they exhibited the same hype as Deep Learning. However, with time and development, they are now on the verge of becoming main-stream and we expect the same for our new friend Deep Learning.

In fact, if we see from the perspective of a DL enthusiast, we will discover that DL has been more than just a hype – it has actually done wonders in diverse fields – from playing games to self-driven cars, DL technology is used in almost everything ‘technological’.

In 2016, an AI-driven Go-playing system won over Korean champion Lee Sodol. Not only did it defeated the opponent but also excelled to become the best of Go, acing the strategy game. Tesla too leverages the Deep Learning technology for their self-driving cars. Next, Amazon’s Alexa is heard to use the divine technology of DL to make love-life predictions. It will suggest you what went wrong between you and your consort.

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Put simply, Deep Learning is the revolutionary new-age technology. Organizations are investing funds and resources all over the world. Considering the current growth rate, DL technology is soon expected to break into the mainstream industry replacing all conventional modes of technology and communications.

Outlook

With AI being the topic of discussion in almost every industry verticals, DL has been gaining popularity. No wonder, it has proved tremendously beneficial in the past but the future expectations are pretty high as well. In this case, we have to wait and observe how Deep Learning manages to fulfil industry expectations and stay inside the ring!

Delhi is home to a bevy of reputable Deep learning training institutes. Browse over their course details and pan out the best from the lot.

The blog has been sourced from ―  www.analyticsindiamag.com/why-is-deep-learning-still-on-the-hype-cycle/

 

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Know the 5 Best AI Trends for 2019

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Know the 5 Best AI Trends for 2019

Artificial Intelligence is perhaps the greatest technological advancement the world has seen in several decades. It has the potential to completely alter the way our society functions and reshape it with new enhancements. From our communication systems to the nature of jobs, AI is likely to restructure everything.

‘Creative destruction’ has been happening since the dawn of human civilization. With any revolutionary technology, the process just speeds up significantly. AI has unleashed a robust cycle of creative destruction across all employment sectors. While this made old skills redundant, the demand and hence acquisition of superior skills have shot up.

The sweeping impact of AI can be felt from the fact that the emerging AI rivalry between USA and China is hailed as ‘The New Space Race’! Among the biggest AI trends of 2018 was China’s AI sector – it came under spotlight for producing more AI-related patents and startups compared to the US. This year, the expectations and uncertainties regarding AI both continue to rise. Below we’ve listed the best AI trends to look out for in 2019:

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AI Chipsets

AI wholly relies on specialized processors working jointly with CPU. But, the downside is that even the most innovative and brilliant CPUs cannot train an AI model. The model requires additional hardware to carry out higher math calculations and sophisticated tasks such as face recognition.

In 2019, foremost chip manufacturers like Intel, ARM and NVidia will produce chips that boost the performance speed of AI-based apps. These chips will be useful in customized applications in language processing and speech recognition. And further research work will surely result in development of applications in fields of automobiles and healthcare.

Union of AI and IoT

This year will see IoT and AI unite at edge computing more than ever. Maximum number of Cloud-trained models shall be placed at the edge layer.

AI’s usefulness in IoT applications for the industrial sector is also anticipated to increase by leaps and bounds. This is because AI can offer revolutionary precision and functionality in areas like predictive maintenance and root cause analysis. Cutting edge ML models based on neural networks will be optimized along with AI.

IoT is emerging as the chief driver of AI for enterprises. Specially structured AI chips shall be embedded on majority of edge devices, which are tools that work as entry points to an entire organization or service provider core networks.

Upsurge of Automated ML

With the entry of AutoML (automated Machine Learning) algorithms, the entire machine learning subject is expected to undergo a drastic change. With the help of AutoML, developers can solve complicated problems without needing to create particular models. The main advantage of automated ML is that analysts and other professionals can concentrate on their specific problem without having to bother with the whole process and workflow.

Cognitive computing APIs as well as custom ML tools perfectly adjust to AutoML. This helps save time and energy by directly tackling the problem instead of dealing with the total workflow. Because of AutoML, users can enjoy flexibility and portability in one package.

AI and Cyber security

The use of AI in cybersecurity is going to increase by a significant measure because of the following reasons: (i) there a big gap between the availability and requirement of cybersecurity professionals, (ii) drawbacks of traditional cybersecurity and (iii) mounting threats of security violations that necessitate innovative approaches. Depending on AI doesn’t mean human experts in the field will no longer be useful. Rather, AI will make the system more advanced and empower experts to handle problems better.

As cybersecurity systems worldwide are expanding, there’s need to cautiously supervise threats. AI will make these essential processes less vulnerable and way more efficient.

Need for AI Skilled Professionals:

In 2018, it was stated that AI jobs would be the highest paying ones and big enterprises were considering AI reskilling. This trend has been carried over to 2019. But companies are facing difficulties trying to bridge the AI skills gap in their employees.

Having said that, artificial intelligence can do wonders for your career if you’re a beginner or advanced employee working with data or technology. In Delhi, you’ll find opportunities to enroll for comprehensive artificial intelligence courses. DexLab Analytics, the premier data science and AI training institute, offers advanced artificial intelligence certification in Delhi NCR. Check out the course details on their website.

 

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CoCalc and Juno Help You Master Data Science on Mobile Phones, Here’s How!

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CoCalc and Juno Help You Master Data Science on Mobile Phones, Here’s How!

Innovation has been at the heart of data science evolution. Cutting-edge technology advancements are found influencing data science training and learning mediums. Besides conventional channels, such as desktops and laptops, there’s now a new way to master machine learning coding systems, i.e. through mobile phones. A robust combination of tools is now at your service to help you code and monitor complex machine learning frameworks using mobile phones.

Take a look at these two tools; they are perfect tools for completing random machine learning tasks.

CoCalc

It is a pioneering web app that hosts coding environments amidst the cloud. It is a sophisticated online work domain that helps you perform mathematical calculations in the cloud. Later, you can share your projects even successfully.

CoCalc is primarily student-friendly software. It is crafted for students’ training modules and machine learning training programs. Thus, it comes loaded with a slew of potent data science packages, including Pandas, and all this makes it easier to develop Jupyter notebooks.

A lot of teachers are found using CoCalc to design courses. People can even chat using CoCalc, which further enhances collaboration on projects and improves the overall learning experience. What’s more, its customer service is also quite responsive. Their team of experts is always a step ahead to assist you.

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Juno

The notable iPhone app helps user code in CoCalc on any mobile devices. In fact, Juno is specially designed for mobile and boasts of superb keyboard support. It tackles multi-screen multitasking challenges and provides support to Python code completion.

Quite interestingly, Juno is largely free for users. That makes it more suitable for mastering demographic. The experts have tried their best to make the free versions of Juno as interactive and fun as possible – engage with introductory notebooks available on Python, Matplotlib, Jupyter, SciPy and NumPy without shelling any extra penny. They not only keep things interesting but also feel good on the pocket.

However, if you want to savour the benefits of Juno Pro that connects you to an arbitrary Jupyter server, you have to make a one-time purchase and use it on all your devices.

Power of Combination

Surely, an effective combination of these two abovementioned tools comes as a soothing balm in the life of working professionals. They are the ones who need to be constantly on the go. Now, with these powerful tools at the tap of their fingers, they can work on myriad data science assignments while being at home or travelling.

However, as a downturn, coding on mobile is not as easy as it seems to be. Mobile devices are not highly configured to support rapid content creation. As a result, they take more time finishing an assignment as compared to laptops and desktops.

But, of course, if you are an adult learner, Juno and CoCalc are sure-fire ways to make progress along the bustling field of artificial intelligence and machine learning. In case, you want to learn more about AI, opt for an encompassing artificial intelligence certification in Delhi NCR.

 

The blog has been sourced from ― www.analyticsindiamag.com/learn-data-science-on-your-mobile-phone-with-these-tools

 

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Advancement in Genomics with Artificial Intelligence

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Advancement in Genomics with Artificial Intelligence

Artificial Intelligence is raging hot, and the healthcare industry is not left behind. Reports suggest AI will help the healthcare industry generate $6.7 billion in revenue. In healthcare, genomics is one of the most notable areas that have evolved significantly after the rise of AI. Involving processes like gene editing and sequencing, genomics is largely performed in agriculture, customized medicine industry and animal husbandry.

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Researchers have long been conducting DNA analysis. However, their initiatives used to be stalled midway because of several challenges – such as the massive size of the genome, high cost, regulatory factors, prediction norms and technology limitations. On top of that, a vast amount of data on genes and genomes further added up to the problem of ostentatiously large amount of patient data. 

Fortunately, today, researchers are better off using machine learning for genomics – they can now perform gene synthesis, construct precision and personalized medicines and understand the genetic makeup of each orgasm amongst others.

Major Development Highlights

  • Elevation Project by Microsoft grabbed eyeballs when its researchers collaborated with a set of biologists from UC Berkeley to assist in gene editing using AI. They decided to combine their efforts and increase efficiency and accuracy of CRISPR technology – which is basically a gene-editing tool for resulting in genetic improvements.

Together, they also launched Elevation, which uses Machine Learning technology to forecast effectively the off-target effects that take place during the process, thus increasing the efficiency of the entire process.

  • Nvidia and Scripps Research Translational Institute (SRTI) improvised their operations for developing deep learning tools and methods. They aim to process and analyze genomic and digital medical sensor data that would increase the use of AI and prevent the spread of diseases, promote health and streamline a host of biomedical research measures.
  • Google released DeepVariant – it is a cutting-edge deep learning model designed to analyze genetic succession. Last year, they devised a new version DeepVariant v0.6, which features brand new accuracy developments that helps get a more accurate picture of an entire genome.
  • Deep Genomics, a budding startup in Canada is found leveraging artificial intelligence to decipher genome and ascertain the most suitable drug therapies based on DNA found on the cell. The company specializes in the field of personalized medicines.

Genomics in India

Following the footsteps of its global partners, India too is slowly maneuvering into the space of AI-powered genomics – several startups, like Artivatic Data Labs are building power in this new field with radical innovations. Another Chennai-based startup, Orbuculum is leveraging AI to predict debilitating diseases and optimize disease diagnosis.

End Note

Major breakthroughs are happening in the new world of genomics. But, of course, understanding human genome and developing genomic medicines is beyond the human capabilities. Often, it needs analysis of millions and millions of data and performs several repetitive tasks for which AI seem to be the most feasible solution. Undeniably, advancements in AI and ML technology have resulted in a comprehensive understanding of genomics – they are the best way to interpret and proceed on genomic data.

FYI: DexLab Analytics is a top-notch artificial intelligence training institute in Gurgaon. It offers excellent in-demand skill training for students, professionals and anyone who is interested in data.

 

The blog has been sourced from ― www.analyticsindiamag.com/when-artificial-intelligence-meets-genomics

 

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Self Healing Machines: The Future Weapon to Fight Cyber Crime

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Self Healing Machines: The Future Weapon to Fight Cyber Crime

Science and technology is the lifeblood of digital success. Connected devices, innovation trends and swift urbanization are transforming the economies for good. But, as it’s said, you have to bite the bitter with the sweet – digital dependency on shared infrastructure comes with its own cons.

“The more machines, the more critical our cyber-security problem becomes. Increased attacker sophistication means devices are now attacked at the deepest levels, including firmware and embedded software. In this new threat landscape, we cannot just rely on manual human intervention. We have to change the paradigm,” shares Boris Balacheff, Chief Technologist for Security Research and Innovation at HP.

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Self Healing Machine is the Answer

For that, conventional security devices won’t be enough – self-healing machines are the answer. They not only detect advanced cyber attacks but also shut off the system completely and restore it later without human intervention. Their response is quick.

As cyber criminals are on the rise, HP strives to reinvent device security to a great extent. For twenty years and more, HP Labs have been working on computer security. Its latest innovation drive is focused on bringing down cyber-resilience at hardware level. Just like the perpetrators employ newer tech capabilities, inventors of today’s security devices should also look up to artificial intelligence – in that respect, self-healing machines should never be only reactive but also proactive. The design and architecture of the machines should be as if they can immediately respond to attacks as well as fix their flaws then and there before someone else does.

Security Is In Focus

The prognosis of the World Economic Forum’s Global Centre for Cybersecurity and the UC Berkeley Center for Long Term Cybersecurity suggests that in the age of innovation and technology modification, security is at the fulcrum of future digital success. It needs combined effort and trust from law enforcement, public and private sector and the entire civil society. In order to build a secure and better cyberspace, we need to strike private-public partnership and work together.

To say the least, government and private players are coming together to meet several challenges transpiring from the intersection of security and innovation. Major breakthroughs in the field of personalized healthcare, 3D digital printing and AI are hitting new highs.

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Fortunately, forecasts say self-healing machines can prove useful across a wide range of operations. For example, they can reduce the load of supporting previous products that still relies on security updates. They can quickly predict malfunctions, even before the device starts showing anomalies. In fact, some of the IoT devices are already attached to vibration and ultrasonic sensors to make monitoring easy.

Conclusion

But, of course, self-healing machines can never be left alone to guard themselves – most of the companies will seek a human in the loop. Humans form an integral role in operating self-healing machines. “We need to continue to reinvent the security of the machines that we will depend upon for years to come,” shares Balacheff. “It’s our only way to win.”

DexLab Analytics offers state of the art artificial intelligence certification in Delhi NCR – the course infrastructure is well fed, student-friendly and follows a practical approach.

The blog has been sourced from ― www.wired.com/brandlab/2018/10/fighting-cybercrime-self-healing-machines
 

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How Artificial Intelligence Can Help Tackle Emotionally Charged Situations

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How Artificial Intelligence Can Help Tackle Emotionally Charged Situations

An increasing number of jobs are incorporating artificial intelligence. But artificial intelligence applications tacking emotional problems is something rarely heard of.

Researches reveal that people who face workplace harassment don’t always report it to concerned authorities– fear of judgment and discomfort in recalling emotionally disturbing situations being among the many reasons. However, scientists believe that people shying away from human assistance might be able to open up in front of a tech.

Let’s look at some examples of artificial intelligence applications assisting humans in emotional situations.

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Spot:

Back in February 2018, Shaw and his partners who are software engineers by profession introduced Spot. This web-based chatbot is powered by artificial intelligence and helps people share and report painful incidents. The app incorporates expertise of psychologists as well as police personnel and has a robust interview technique ensuring recorded narratives are highly accurate. Moreover, the person interacting has the option to remain anonymous.

Spot is trained to gather information from the person’s initial description of the incident and respond to cues by posing specific questions, but never asking leading questions that might intimidate the user. Finally, a detailed and time-stamped PDF report is generated, which the user can choose to share or keep personal.

Advantages of artificially intelligent tools dealing with human emotions:

  • Apps are accessible 24/7
  • No prior appointment with HR is needed
  • Eliminate inconsistencies associated with human interactions
  • Help overcome a common human weakness– emotional memory recall
  • Machines can act more logically in situations where humans fail to think clearly.

Navigating through human emotions:

Ixy is another AI based app that aims to minimize anxiety in human chats by facilitating interactions over texts. It samples texts to help users understand how he/she is perceived by others.

Israeli enterprise Beyond Verbal has launched ‘’emotional analytics’’ software, which is a patented technology that measures the emotional factor of voice based on its modulation. This tech is employed in call centers so worker interactions can be fine-tuned as per customer needs. Other important uses of this software include examining employee morale and improving the working of AI virtual assistants by helping understand elements between lines of human conversations.

Yoram Levanon, the inventor of Beyond Verbal tech and its chief science officer, visualizes the development of applications more advanced than the ones described above, such as virtual assistants that can examine vocal biomarkers and predict a person’s emotional and physical state. He firmly believes that modern AI apps need to work in tandem with human emotions; that’s the key for AI to be complementary to human work and not its replacement.

To help, not replace humans:

Recently, there’s been a lot of unease and insecurities associated with AI. There’s fear that AI might eat into our human side and corrode human emotions. But the opposite of this is actually possible and these apps prove that point. Done correctly, machines can help us become better humans.

In today’s tech-driven society, the first step in unlocking the powers of modern equipment is enrolling for comprehensive Artificial Intelligence Certification Courses. From factory machinery, to chatbots, and now even human emotions, everything is forming inseparable bonds with AI. Join the leading artificial intelligence training institute in Gurgaon DexLab Analytics and begin a successful journey in this field. For course details, look up the brochure on DexLab’s website.

 

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