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Top 5 AI-based Applications for Crime Prevention and Detection

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Top 5 AI-based Applications for Crime Prevention and Detection

Companies and cities across the globe are attempting to employ AI in a plethora of ways to address crime. Day by day, city’s infrastructure is becoming smarter and tech-efficient. Crime detection is no more a catch-22. With easy availability of real time information, it’s now easier to detect crimes.

Here, we are going to dig into a few present AI applications in crime detection and prevention:

Gunfire Detection – ShotSpotter

ShotSpotter utilizes smart city infrastructure to pinpoint the area from where the gunshot came through. The company representatives claim that their system has the ability to alert authorities in real time with the data about what kind of gunfire it was and the exact location as accurate as 10 feet. Thanks to multiple sensors and their machine learning algorithm. They work by picking up the sound of the gunshot.

At present, they are being used in over 90 cities across the world, including Chicago, New York and San Diego.

AI Security Cameras – Hikvision

China’s top notch security camera producer, Hikvision made an announcement last year: they are going to use chips from Movidius (an Intel company) to develop cameras that would run intricate, deep neural networks right away.

They claim this new camera would better scan the license plates on cars, perform facial recognition for potential criminals and automatically identify suspicious anomalies. Currently, their advanced visual analytics systems can achieve 99% accuracy and with 21.4% of market share for CCTV and Video Surveillance Equipment worldwide, Hikvision has clearly secured a respectable position in the video surveillance space.

Predict crime locales – Predpol

Predicting future crime spots is no mean feat! But Predpol is proud to venture into that nuanced area with their powerful big data and machine learning capabilities that can predict the time and location new crimes are most likely to happen. And that can be done through data analysis of past crimes. Historical data plays an integral part in building such algorithms.

Los Angeles is one of the American cities that have adopted their system, among others.

Who commits the crime – Cloud Walk

Cloud Walk, the Chinese facial recognition enterprise is foraying into a new scope of technology where it would be possible to predict if a person decides to commit a crime, even before he attempts to. As a result, they have built a system to detect suspicions changes in the manner or behavior of an individual. For example, if a person buys a hammer, that’s completely fine. But of course, if he buys a knife and a rope, he comes under the radar of suspicion.

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Find suspects most likely to commit another crime – Hart

If you know, the individuals charged of a crime are soon released until they stand trials. Now, deciding who should be released pre-trial is like being in deep water. For that, Durham, UK has employed AI technology to enhance their current system of deciding which suspect to release. The program is called Harm Assessment Risk Tool (Hart), and is fed with 5 years’ worth of criminal data for smoother prediction of a person’s vulnerability towards crime.

A whole body of data is used to predict whether an individual falls under the purview of low, medium or high risk. Comparing the prediction with the real world results, we found out that most of the predictions of HART were close to being accurate.

The robust growth of AI and machine learning is the best thing since sliced bread. Their superior technology for crime detection is already in place, and is growing to expand further in the future.

Keeping that in mind, we at DexLab Analytics offer a bunch of Machine Learning Using Python courses to shape your future for good. Our Machine Learning Courses are of top quality and fits the budget of all.

The article has been sourced from – https://www.techemergence.com/ai-crime-prevention-5-current-applications/

 

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Top 4 Applications of Cognitive Robotic Process Automation

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Top 4 Applications of Cognitive Robotic Process Automation

With the dawn of automation, industries all over the world are depending on robots to carry out tasks, such as product designing and manufacturing. It optimizes repetitive processes and improves cost efficiency. Incorporation of cognitive capabilities, like natural language processing and speech recognition into robotic process automation has resulted in the birth of Cognitive Robotic Process Automation (CRPA). Let’s delve into the current applications of this revolutionary technology.

Finance and banking sector:

Customers are demanding expedient methods to transfer money and make investments.  Also, the volumes of customer data are increasing rapidly. Hence, banks need to improve the speed of information processing. To achieve this, they have turned to process automation. Many banks are adopting AI-powered technology to automate regular processes.

According to a survey conducted by BIS Research, Banking and Finance sector is likely to become the largest revenue generator in the world for CRPA industry. For example, Bank SEB in Sweden bought cognitive robotic process automation software from IPsoft, a foremost company of CRPA industry. This technology is actually a software robot named Amelia that has knowledge of 20 different languages and is aware of semantics, including English and Swedish. In case Amelia fails to solve the problem at hand, it transfers the same to a human operator, and studies the interaction to hone its skills and apply it to similar cases in future.

U.K.’s KPMG has collaborated with Automation Anywhere to provide digital staff for clients.

Insurance:

Task like manual inputs, data gathering and retrieval, legacy applications and system updating is very time consuming. Hence, the insurance industry is welcoming automation in its processes. This help with the following tasks:

  • Automates fraud detection, policy renewal and premium calculation
  • Improves customer service
  • Enhances employee engagement
  • Upgrades business productivity as software robots can work for hours at a stretch
  • Frees employees for important tasks that need manual handling

Developed economies, including U.S. and the European nations are extensively employing RPA/CRPA bots. AXA Group, one of the chief French insurance companies using smart automation services to improve its bankroll, reported that France has the fifth highest insurance premiums in the world.

Leading IT service provider of Australia, DXC Technology, has partnered with Blue Prism, one of the best companies providing RPA solutions, to improve the RPA capabilities for key insurance clients, like Australia and New Zealand Banking Group (ANZ). Fukoku Mutual Life Insurance, top insurance firm of Japan, has replaced 30 human workers with IBM’s latest AI tech, Watson Explorer. The tech’s deployment has boosted company savings and enhanced productivity by 30%.

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Telecom and IT Industry:

Business process outsourcing (BPO) services are facing problems like increased operational costs and low profit margins. RPA/CRPA software bots can be one of the ways to tackle this problem. Hexaware Technologies, a topnotch company in this field, has partnered with Workfusion to evolve IT infrastructure, combat the aforementioned problems and boost overall productivity.

Healthcare:

Some of the challenges of the healthcare industry are:

  • Maintaining paper records of patients’ medical documents.
  • Transferring these records to digital databases
  • Manually updating databases
  • Maintain an inventory database for medicinal supplies
  • Systematic management of unstructured data
  • Innovation in healthcare encounter regulatory and reporting challenges when launching new drugs.

These tasks are repetitive and increase chances of errors when done manually. Automation helps tackle these problems and also provide safe and good quality drugs to the market. Blue Prism is one of the principal providers of RPA for healthcare.

Future Scope:

Competition in the global capital markets is increasing. New contestants are bringing in ‘’disruptive technologies’’ that are pressurizing existing institutes to increase their efficiency and cut down costs. Hence, the need to embrace cognitive automated technology.

Australia and Japan are among the top countries adopting process automation. Leading countries embracing RPA for financial services include India, China and Singapore. It is expected that Fintechs will mainly disrupt three areas of financial sector-consumer banking, investment handling, fund and payment transfer.

It is about time that all businesses and organizations integrate machine learning and artificial intelligence in their processes for competitive advantage.

How can you take advantage of this tech-driven era? Enroll for machine learning training in Delhi at DexLab Analytics. Many top companies look for expertise in this budding technology while recruiting employees. DexLab’s Machine learning course in Delhi offers superior guidance that will help you develop crucial knowledge needed to stay ahead of competition.

 

Reference link: https://www.techemergence.com/cognitive-robotic-process-automation-current-applications-and-future-possibilities

 

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How Some Astonishing Techs of 2018 are Influencing Development in Africa

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How Some Astonishing Techs of 2018 are Influencing Development in Africa

The technological evolution is conquering things which we previously considered to be strictly human. Addressing the scope of current tech space, we can say that the possibilities are endless, quite literally. AI is accomplishing tasks we never thought were feasible, such as composing music and creating videos. With the help of analytics, doctors can predict the effectiveness of cancer treatments in days; whereas earlier precious months were wasted to determine the same. In Africa, AI-powered technology will soon be employed to tackle burning social problems.

So, let’s take a look at some current tech trends that are inspiring development in Africa.

New data sources:

The availability of new sources of data plays a pivotal role in current tech advancements. For example, consider the financial inclusion of a nation’s population. Traditionally, financial institutes analyze financial history of an individual to determine if the person is eligible for a loan or not; which includes credit scores, IDs and other relevant documents. However, this excluded a large portion of the population who never had the chance to build a credit history due to the absence of any form of documentation. FinTech companies in East Africa have adopted a different approach-they are analyzing available mobile data about a person, like the frequency of getting a recharge, and making lending decisions on the base of this data. Thus, more people are now being able to access financial services and accomplish their goals, like educating children or starting businesses.

New features in SAS platform have the ability to analyze images. This is benefiting rangers working for wildlife conservation as previously they would have to manually sort the pictures of animals into species and sexes. Now, SAS’s new AI-driven technology can do the classification and rangers can focus on more important tasks.

Improved predictions:

Africa is seeing the emergence of new machine learning algorithms, like the extreme gradient boosting model, which are allowing data scientists to make more precise predictions. In Nigeria, this is boosting the development of models that prevent customer churn in the telecom industry. These models assess customer information, like billing data, purchase history, demographics and service usage, and create loyalty profiles that enable better marketing campaigns.

Bring into play the unstructured data pool:

Generally, companies crunch data from structured data sources, like transactional data. However, tapping into unstructured data sources, like customer complaints, reviews and text information, can be highly advantageous for businesses. These data sources help predicting customer churn more accurately.

Plunging into Deep Learning:

Deep learning falls under the category of machine learning, which is creating waves of excitement all over the world. It has the ability to model complex concepts in data through the use of high-level structures, algorithms and multiple processing steps. Deep learning teaches computers to recognize patterns through the numerous processing steps and perform tasks that are conventionally carried out by humans, such as image identification and speech recognition.

These models are improving traditional techniques used in credit risk modeling and fraud detection. SAS has collaborated with Equifax to implement deep learning models for improved risk management.

Nigeria has turned its focus on upskilling its people in data science, so that they can take advantage of this AI-era and become an outsourcing hub for deep learning projects.

Emotionally intelligent bots:

An exciting application of AI and language processing is chatbots. They are programmed to enable conversations between machines and humans. This helps save a lot of time and money that was previously wasted on performing repetitive and mundane tasks, such as responding to customer queries related to their bank accounts.

Recently, United Bank of Africa launched its chatbot, named Leo, which is in fact a Facebook bot that allows bankers to carry out real-time transactions and other banking activities, like opening accounts and paying bills.

Thus, we are entering an era where machines can think and learn utilizing the power of AI. AlphaGo, a programme created by Google in 2016, has been able to defeat the best human players of this ancient Chinese game. And AlphaGo Zero, the next version, learned by playing against itself and after a period of time defeated AlphaGO.

To read more blogs on current technologies, follow Dexlab Analytics– we provide the best Machine Learning training in Delhi. Take a look at our machine learning courses in Noida.

 

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

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Microsoft Introduces FPGA Technology atop Google Chips through Project Brainwave

A Change Is In the Make – due to increasing competition among tech companies working on AI, several software makers are inventing their own new hardware. A few Google servers also include chips designed for machine learning, known as TPUs exclusively developed in-house to ensure higher power and better efficiency. Google rents them out to its cloud-computing consumers. Of late, Facebook too shared its interest in designing similar chips for its own data centers.

However, a big player in AI world, Microsoft is skeptical if the money spent is for good – it says the technology of machine learning is transforming so rapidly that it makes little sense to spend millions of dollars into developing silicon chips, which could soon become obsolete. Instead, Microsoft professionals are pitching for the idea of implementing AI-inspired projects, named FPGAs, which can be re-modified or reprogrammed to support latest forms of software developments in the technology domain.  The company is buying FPGAs from chip mogul, Intel, and already a few companies have started buying this very idea of Microsoft.

This week, Microsoft is back in action with the launch of a new cloud service for image-recognition projects, known as Project Brainwave. Powered by the very FPGA technology, it’s one of the first applications that Nestle health division is set to use to analyze the acuteness of acne, from images submitted by the patients. The specialty of Project Brainwave is the manner in which the images are processed – the process is quick as well as very low in cost than other graphic chip technologies used today.

It’s been said, customers using Project Brainwave are able to process a million images in just 1.8 milliseconds using a normal image recognition model for a mere 21 cents. Yes! You heard it right. Even the company claims that it performs better than it’s tailing rivals in cloud service, but unless the outsiders get a chance to test the new technology head-to-head against the other options, nothing concrete can be said about Microsoft’s technology. The biggest competitors of Microsoft in cloud-service platform include Google’s TPUs and graphic chips from Nvidia.

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At this stage, it’s also unclear how widely Brainwave is applicable in reality – FPGAs are yet to be used in cloud computing on a wide scale, hence most companies lack the expertise to program them. On the other hand, Nvidia is not sitting quietly while its contemporaries are break opening newer ideas in machine learning domain. The recent upgrades from the company lead us to a whole new world of specialized AI chips that would be more powerful than former graphic chips.

Latest reports also confirm that Google’s TPUs exhibited similar robust performance similar to Nvidia’s cutting edge chips for image recognition task, backed by cost benefits. The software running on TPUs is both faster and cheaper as compared to Nvidia chips.

In conclusion, companies are deploying machine learning technology in all areas of life, and the competition to invent better AI algorithms is likely to intensify manifold. In the coming days, several notable companies, big or small are expected to follow the footsteps of Microsoft.

For more machine learning related stories and feeds, follow DexLab Analytics. It is the best data analytics training institute in Gurgaon offering state of the art machine learning using python courses.

The article has been sourced from – https://www.wired.com/story/microsoft-charts-its-own-path-on-artificial-intelligence

 

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

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Python Machine Learning is the Ideal Way to Build a Recommendation System: Know Why

In recent years, recommendation systems have become very popular. Internet giants, like Google, Facebook and Amazon, use algorithms to tailor search results to customer preferences. Any system that has a search bar collects data on a customer’s past behavior and likings, which enable these platforms to provide relevant search results.

All businesses need to analyze data to give personalized recommendations. Hence, developers and data scientists are investing all their energies and mental faculties to come up with perfect recommendation systems. Many of them are of the opinion that Python Machine Learning is the best way to achieve this. Often, building a good recommendation system is considered as a ‘rite of passage’ for becoming a good data scientist!

Delving into recommendation systems:

The first step in the process of building a recommendation system is choosing its type. They are classified into the following types:

  • Recommendation based on popularity:

This is a simplistic approach, which involves recommending items that are liked by the maximum number of users. The drawback of this approach is its complete exclusion of any personalization techniques. This approach is extensively used in online news portals. But in general, it isn’t a popular choice for websites because it bases popularity on entire user pool, and this popular item is shown to everyone, irrespective of personal choice and interest.

  • Recommendation based on algorithms:

This process uses special algorithms that are tailor-made to suit every customer. They are of two types:

  • Content based algorithms:

These algorithms are based on the idea that if a person likes a product then he/she will also like a similar product.  It works efficiently when it is possible to determine the properties of each product. It is used in movie and music recommendations.

  • Collaborative filtering algorithms:

These algorithms are dependent on past behavior and not on properties of an item. For example, if a person X likes items a, b, c and another person Y likes items b, c, d, then it is concluded that they have similar interests and X should like item d and Y should like item a. Because they are not dependent on additional information, collaborative filtering algorithms are very popular. E-commerce giants, like Amazon and Flipkart, recommend products based on these algorithms.

After choosing the type of recommendation system to build, developers need to locate relevant datasets to apply to it. The next step is determining the platform where you’ll build your recommendation system. Python machine learning is the preferred platform.

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Advantages of using Python Machine Learning:

  • Code: Python makes the process of writing code extremely easy and working with algorithms becomes quite convenient. The flexible nature of this language and its efficiency in merging different types of data sets make it a popular choice for application in new operating systems.
  • Libraries: Python encompasses a wide range of libraries in multiple subjects, such as machine learning and scientific computing. The availability of a large number of functions and methods enables users to carry out several actions without having to write their own codes.
  • Community: Python includes a large community of young, bright, ambitious and helpful programmers. They are more than willing to provide their valuable inputs on different projects.
  • Open source: The best part about Python is that it is completely open source and has sufficient material available online that will help a person develop skills and learn essential tips and tricks.

Proficiency in Python is highly advantageous for anyone who wants to build a career in the field of data science. Not only does it come handy in building complicated recommendation systems, it can also be applied to many other projects. Owing to its simplicity, Python Machine Learning is a good first step for anyone who is interested in gaining knowledge of AI.

In the current data-driven world, knowing Python is a very valuable skill. If one’s aim is to collect and manipulate data in a simple and efficient manner, without having to deal with complicated codes, then Python is the standard.

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How is AI Shaping the Indian Job Market?

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How is AI Shaping the Indian Job Market?

Currently, startups focusing on Artificial Intelligence, Machine Learning and Deep Learning are on the rise in India. According to a recent report by AI Task Force, there are 750 startups in India that are actively working to build a robust AI ecosystem in India. Initiatives to promote AI by Indian government include establishment of NITI Aayog, the policy think tank of India, and Digital India, which is a campaign to improve technological infrastructure of the country.

65% of participants of a PwC survey believed that AI will have a grave impact on the employment scenario of India. Interestingly, the majority of participants of this survey were of the opinion that AI will allow employees to do more value-added tasks as it will take up all the daily mundane tasks.

Deep Learning and AI using Python

Job market outlook:

‘’We expect a 60 per cent increase in demand for AI and machine learning specialists in 2018’’, said BN Thammaiah, Managing Director, Kelly Services India. Belong, a Bengaluru-based outbound hiring firm startup, shares the same view, stating that the demand for AI professionals has risen by leaps and bounds due to the widespread adoption of AI and automation technologies across companies. Consulting industry leader, Accenture, expects AI to add $957 to India’s GDP by 2035.

Jump in demand:

Only 4 percent of AI professionals have work experience in core domains, like deep learning and neural networks.

For every 1000 jobs in the field of Deep learning, there are approximately 530 professionals available. Similarly, for every 1000 jobs in the field of Neuro-linguistic Programming (NLP), there are only 710 professionals available.

The lack of core data science disciplines in engineering institutes across the country is responsible for the disparity between demand and supply of AI professionals. Only a few selected institutes, like IITs and IISc, have ML programs in their curriculum. The active AI researchers in India are a meager 386 in number.

AI hotspots in India:

AI-work hubs in India are Bengaluru, New Delhi and Mumbai. IBM, Microsoft, Flipkart and Amazon are carrying out good research work in AI. Companies like Adobe, Accenture, Amazon, JP Morgan, SAP, L&T Infotech, Nvidia, Intel and Wipro are actively hiring AI professionals. The main sectors fostering AI employment are e-commerce, banking and finance. Kamal Karanath, Co-founder of Xpheno, a recruitment company, said that there would be a huge demand for AI engineers in these sectors in the next 5 years. AI-powered technology boosts efficiency and security of Indian banking and financial sector.

India Inc is endeavoring to upskill workers in subjects like machine learning, cloud computing and big data. In efforts to nurture talent and obtain solutions from vertical focused AI startups, which are developing innovative technologies, enterprises have set up many accelerator programs. Flipkart is developing AI products that will boost their business growth.

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A peek into the future of AI:

The Indian government intends to establish research institutes and Centres of Excellence that foster training and skilling in fields like AI, robotics, big data analysis and internet of things. Top engineering schools, like IITs, IIITs and IISc are collaborating with industries to bridge the gap in AI talent, provide targeted solutions and steer growth of the AI industry. Government of India is framing numerous policies to promote industry-academic partnerships.

Get an edge in this AI-era by enrolling yourself for the Machine Learning training course at DexLab Analytics– a leading data analyst training institute in Delhi.

 

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How Conversational AI and Chatbots are Revolutionizing Indian banking Industry

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Thanks to the advancements in AI and ML, bank work can now be done with the click of a phone button! Innovations in the field of customer services form an important part of the technology overhaul. The banking sector is making hefty investments on AI technology to simplify user experience and enhance overall performance of financial institutes.

Let’s take a look at how conversational AI and chatbots are revolutionizing the Indian banking industry.

  • Keya by Kotak Mahindra Bank

Keya is the first AI-powered chatbot in Indian banking sector. It is incorporated in Kotak’s phone-banking helpline to improve its long-established interactive voice response (IVR) system.

‘’Voice commands form a significant share of search online. In addition, the nature of the call is changing with customers using voice as an escalation channel. Keya is an intelligent voicebot developed keeping in mind the customers’ changing preference for voice over text. It is built on a technology that understands a customer’s query and steers the conversation to provide a quick and relevant response”, says Puneet Kapoor, Senior Executive Vice President, Kotak Mahindra Bank.

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  • Bank of Baroda chatbot

Akhil Handa, Head of Fintech Initiatives, Bank of Baroda said that their chatbot will manage product-related queries. He believes that the services of the chatbot will result in better customer satisfaction, speedy responses and cost minimization.

  • Citi Union Bank’s Lakshmi Bot

Lakshmi, India’s first humanoid banker is a responsive robot powered by AI. It can converse with customers on more than 125 topics, including balance, interest rates and transactional history.

  • IBM Watson by SBI

Digital platforms of SBI, like SBI inTouch, are utilizing AI-powered bots, such as IBM Watson, to enhance customer experience. SBI stated that modern times will witness the coexistence of men and machines in banks.

  • AI-driven digital initiatives by YES Bank in partnership with Payjo

Payjo is a top AI Banking platform based out of Silicon Valley in California. YES Bank has partnered with Payjo to launch YES Pay Bot, its first Bot using AI, which improves already popular wallet services. The YES Pay wallet service is trusted by more than half-a-million customers.

  • YES TAG chatbot

YES TAG chatbot has been launched by YES Bank and enables transactions through 5 messaging apps. Customers can carry out a wide range of activities, such as check balance, FD details, status of cheque, transfer money, etc. It is currently used in Android and will soon be available on Apple App Store.

  • Digibank

Asia’s largest bank, DBS Bank, has developed Digibank, which is India’s first mobile bank that is ‘chatbot staffed’. It provides real-time solution to banking related issues. This chatbot employs a trained AI platform, called KAI, which is a product of New York startup- Kasisto.

  • Axis Bank launches intelligent chatbot in association with Active.ai

Axis Bank facilitates smart banking with the launch of a chatbot that employs conversational interface to offer interactive mobile banking solutions. This intelligent chatbot was developed in association with Singapore based AI company- Active AI.

  • HDFC Bank launches OnChat in partnership with Niki.ai

To enable smooth ecommerce and banking transactions, HDFC in partnership with Niki.ai has launched a conversational chatbot, called OnChat. It is available on Facebook messenger even to people who aren’t HDFC customers. Users can recharge phone, book cabs and pay utility bills through this chatbot.

  • EVA by HDFC Bank

EVA is exclusively for the customers of HDFC Bank. It is an electronic virtual assistant developed in partnership with Senseforth, an AI startup based in Bengaluru.

  • mPower by YES Bank

mPower is a chatbot for loan products that has been developed by YES Bank in association with Gupshup-a leading bot company. It assists customers on a variety of loan related topics like personal loans, car loans and loan against securities.

In the future, there will be three kinds of bots- speech-based bot, textbots and video chatbots. Conversational bots work in harmony with human employees to enrich customer experience.

Thus, AI-powered technology is the way forward. To be industry-ready in this AI-era, enroll for the Machine Learning course in Gurgaon at Dexlab Analytics. It is a premier Analytics training institute in Delhi.

 

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How Artificial Intelligence is Boosting the Design of Smarter Bionics

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How Artificial Intelligence is Boosting the Design of Smarter Bionics

Artificial Intelligence and Machine Learning are a set of technologies that empower machines and computers to learn and evolve upon their own learning through constant reiteration and consistent data bank upgrades. The entire chain of mechanism stands on recursive experiments and human intervention.

Bionics

Advances in technology have greatly benefited the field of prosthetics in the last few years. Today’s prosthetic limbs are made using space-age materials that provide increased durability and function. In addition, many prosthetics make use of bionic technology. These types of prosthetics are called myoelectric prosthetics.

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Myoelectric prosthetics picks up the electrical action potential from the residual muscles in the amputated limb. Upon receiving the action potentials, the prosthetic amplifies the signal using a rechargeable battery. Detected signal can be used further into usable information to drive a control system. Artificial Intelligence helps to identify motion commands for the control of a prosthetic arm by evidence accumulation. It is able to accommodate inter-individual differences and requires little computing time in the pattern recognition. This allows more freedom and doesn’t require the person to perform frequent, strenuous muscle contractions. The inclusion of AI technology in prosthetics has helped thousands of amputees return to daily activities. While technologies that make bionic implants possible are still in infancy stage, many bionic items already exist.

The Bionic Woman

 Angel Giuffria is an amputee actress and Ottobock Healthcare’s Brand Champion who has been wearing electromechanical devices since she was four months old. Following are excerpts of an interview with her.

“I wear currently the bebionic 3 small-size hand which sounds like a car. But at this point, that’s where we’re getting with technology. It’s a multi-articulating device. That small-size hand is really amazing… this technology wasn’t available to them previously”

She further added, “..The new designs that look more tech are able to showcase the technology. I’ve really become attached to and I think a lot of other people have really clung onto as well because it just gives off the impression of showing people how capable we are in society now.”

 She also spoke about prosthetics like the Michelangelo Hand which is stronger, faster and has multiple hook options. Modern additions to prosthetics such as lights and cameras are added advantages. She describes her hand to be able to do multiple functions like change grip patterns and control wrist movements which enable her to hold small items like keys and credit cards.

angelgiuffriaottobokbebionichand

I-limb

Bertolt Meyer’s amazing bionic hand controlled via an iPhone app is another glimpse at the advances being made in prosthetics.

In 2009, Meyer, a social psychologist at the University of Zurich was fitted with an i-limb, a state-of-the-art bionic prosthesis developed by a Scottish company, Touch Bionics, which comes with an aluminum chassis and 24 different grip patterns. To select a new suite of gestures, Meyer simply taps an app on his iPhone. He describes his i-limb to be the first, where aesthetics match engineering.

Bertolt-Meyer-who-has-an--010

In the world of prosthetics, function is the key. Most amputees are constantly searching for the same level of functionality that they enjoyed before they lost their limb. With the introduction of artificial intelligence in prosthetic limbs, amputees are closer to their goals than ever before. Bionics having access to the relevant databases are capable of learning new things in a programmed manner which improves their performance.

For more such interesting blogs follow Dexlab Analytics. Also take a look at the Machine Learning courses being offered by Dexlab Analytics– a premier analytics training institute in Gurgaon.

 

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Here’s Why Automation Is Gaining Accolades All over the World?

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Here’s Why Automation Is Gaining Accolades All over the World?

Over the last few decades and more, if there’s anything that has continuously evolved, surprising us with something new at each turn, it’s TECHNOLOGY. Technological advancements have come to a point where people and businesses have started relying on Automation.

Automation is the technology by which a process is performed entirely by means of various control systems and equipments without human assistance. The purpose is to enhance productivity, deliver faster results and save costs, depending on the industry and the extent to which automation is being applied.

Automation, which is mostly about streamlining the work process, can take place either by means of implementing intelligence into the existing systems or by replacing the same. Some of these systems operate without human interference while others support humans and pave ways for better productivity.

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Leveraging Automation for Good

How to introduce automation into the workflow and the procedure of doing that has been a matter of concern for most entrepreneurs. Putting machines to work requires certain considerations. Following are a few smart tips for introducing automation to your business and leveraging the most out of it.

  • Putting the machines to work for you necessitates you to implement the automation tools gradually. This should always be followed by a close evaluation of the existing process so that it is applied to where streamlining is needed the most.
  • Automation is an instant success when you start things manually, but sometimes things can take a downturn if you lack enough knowledge of the domain. The switch to automation should be planned ahead with care.
  • Automation is a change that most people will be reluctant to adopt at least initially. So, it is always better to ensure that there is a solid change management strategy in place to make the technology actually make lives easier and not otherwise.

Automation isn’t something NEW ON THE BLOCK

The very notion of automation has been in the minds of entrepreneurs as well as traditionalists for around centuries. For an example, an elevator that’s been in existence for so many years is an automation tool for mobility. Recently, massive breakthroughs have been achieved in this budding field, and cloud computing is one of them. It has driven the cost of storage solutions really down, while pushing machine learning skills to a new high.

In short, while automation has always been more of a IT tool, it’s now finding its footing in other domains within an organization. “Whether it’s a catalog or an app, it now allows me as a consumer to control things when they’re happening,” says Marc Wilkinson, CTO for Workplace and Mobility at DXC.

Automation and employment crunch

As it’s said, automation isn’t about eliminating jobs instead it’s more about creating efficiencies. If the work done in twice the speed, it would be more efficient and employer will benefit from productivity gains and the employees in the event will hit off high-impact projects.

So, in a sense, automation won’t entirely result in lower employee headcount, though it may affect a certain kind of employment. High-touch occupations will witness limited impact. For an instance, hospitals have to very much human-to-human; they can’t go totally automized.

In case, you are thinking of having a career in automation, get enrol in a good Machine Learning Certification course. DexLab Analytics offers some of the best machine learning training in Delhi, NCR region: go check out the course itinerary.

 

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