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IR19

Everything You Need to Know About Deep Learning

Over the past couple of years, deep learning has strongly worked its way into the world of business. Often, deep learning is confused with machine learning and the terminologies are even used interchangeably. However, it is important to understand that both machine learning and deep learning are very different terms with independent definitions and functionalities.

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Deep learning is basically machine learning on a more in-depth level. This technology has taken inspiration from the way the human brain works. It requires high-end machines with distinct add-in graphics chips that are capable of crunching numbers and large volumes of data. In deep learning, a computer model learns to perform classification tasks directly from text, images, or sound. Interestingly, deep learning capabilities can achieve extremely high accuracy and precision, sometimes exceeding human-level performance. The models are trained by using a large set of labeled data and neural network architectures that contain many layers. Deep learning is a key technology behind one of the most recent technology trends that have slowly started to hit the rods – driverless cars. This technology enables cars to recognize a stop sign or to distinguish a pedestrian from a lamppost. They also are widely becoming popular for several other use cases such as the voice control in consumer devices like phones, tablets, TVs, and hands-free speakers.  

Deep learning is basically machine learning on a more in-depth level. This technology has taken inspiration from the working of a human brain Click To Tweet

How is deep learning different from machine learning?

Unlike machine learning algorithms that break down problems into smaller parts and solve them individually, deep learning solves the problem from end to end. Also, the amount of data and time that has been fed to a deep learning algorithm is directly proportional to its efficiency at solving a task. Unlike machine learning, in deep leaning, data isn’t provided for the program to use. Instead, all pixels within an image are scanned to discover edges that can be used to distinguish between different elements. Following this, the edges and shapes are sorted into a ranked order of possible importance to determine the different elements. Furthermore, this process requires more hardware to process the data generated by the algorithm. Programs using deep learnings algorithms also take longer to train compared to machine learning programs as they are learning on their own instead of relying on hand-fed shortcuts.

How does deep learning work?

This technique involves feeding a computer system with a lot of data, which later helps it to arrive at decisions regarding other data. This model is designed to continually analyze data with a logic structure similar to how a human would draw conclusions. This is achieved by using a layered structure of algorithms called an artificial neural network (ANN). This helps in creating machine intelligence that’s far more superior in terms of capability than that of standard machine learning models. Deep learning can be applied to any form of data including written words, video, audio, speech, and machine signals. They produce conclusions that are as accurate as the ones made by humans. 

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What are some of the key applications of deep learning?

Deep learning has an array of impressive applications. Let’s take a look at some of the most prominent ones:

  • Self-driving carsThough self-driving cars are in their early stages of testing, there have been several advanced developments in the way they have been performing. With deep learning, cars are learning to recognize obstacles and react to them appropriately with the help of sensors and onboard analytics.
  • Precision medicineThe field of healthcare has advanced to unfathomable levels. Now, healthcare professionals are looking at developing medicines that are genetically tailored to an individual’s genome by using deep learning techniques.
  • Recoloring black and white images – Black and white pictures and videos can be given back their original color by using this technology to teach computers to recognize objects and learn what they should look like to humans.Get More Info

  • Gaming – Several gaming companies have been using deep learning capabilities. These systems have been taught to play and even win games.
  • Analysis and reporting – Deep learning systems are being used to analyze data and derive actionable and useful insights from them, accompanied with infographics which we can be easily comprehended.
  • Predicting the outcome of legal proceedings – Recently, a team of American and British researchers has developed a system that could correctly predict a court’s decision, when fed the basic facts of the case.
  • Cancer research  Cancer researchers are using this technology to automatically detect cancer cells. Researchers have built an advanced microscope that provides a high-dimensional data set that can be used to train applications to accurately identify cancer cells.

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4 Major Artificial Intelligence Problems You May Not Know About

Artificial intelligence has taken over the world incredibly fast. AI technology is being used across all industries today; thereby, reducing the dependency on human labor and minimizing the errors and delays made in operations. Artificial intelligence is a broad term which incorporates capabilities ranging from image recognition software to robotics. The maturity level of each of these technologies differs from one another. But the catch here is that introducing any advanced technology into an industry comes with its own set of challenges. Artificial intelligence problems are often overshadowed by their numerous benefits; however, these challenges cannot be completely ignored. It is always better for companies to think of the flip side of a technology to understand the gaps and challenges involved. 

KeRequest Proposaly Artificial Intelligence Problems

Varying development approach

One of the most prominent artificial intelligence problems is that of the development approach. The development phase is quite different in the case of artificial intelligence. Most of the time, developments in AI technology are all about identifying data sources and then gathering content, cleansing it, and then curating it. Such an approach requires different skills and mindsets, as well as different methodologies. In addition, AI-powered intellectual systems must be trained in a particular domain.

Highly dependent on data

Artificial intelligence heavily relies on data for learning. They depend on enormous amounts of high-quality data from which to observe trends and behavior patterns. Furthermore, AI systems quickly adapt to improve the accuracy of the conclusions derived from the analysis of that data. However, the massive amount of data required becomes one of the most challenging artificial intelligence problems for companies. Also, the datasets need to be highly representative and balanced, failing which, the system will eventually adopt bias that is contained in the data sets.

Experimental natureDemo

Next among the artificial intelligence problems is the difficulty in predicting the ROI and the improvements it may bring to a project. The outcomes of artificial intelligence are highly dependent on the data that has been fed into the system. It also requires a skilled team that can write or adapt to publicly available algorithms, select the right algorithm for the desired result, and combines algorithms as needed to optimize the result.

Threat to privacy

Like in the case of most technology, privacy breach is one of the major artificial intelligence problems. AI technology that recognizes speech and has the capability to decipher natural language will theoretically be able to understand each conversation that takes place on e-mails and telephones. This means that if the system gets hacked, the user’s data could get leaked easily. Also, any malfunction in the system could also result in the loss of an enormous amount of data, some of which cannot even be recovered.


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Top Trends in Artificial Intelligence for 2018

Key Technologies That Are Integral to Artificial Intelligence

Answers to the 3 “Big Questions” in Artificial Intelligence

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Top Trends in Artificial Intelligence for 2018

The world was in awe and threw in a lot of praise for Google’s new AI voice assistant Duplex which is able to place a call to a human on behalf of another human. This just goes to show the level of advancement AI has achieved this year. However, Google Duplex is just one demonstration amongst numerous other tech trends, areas where artificial intelligence has been making headways. Every day a new headline is made with AI affecting certain aspects of life. But what is happening behind the scenes, in development facilities where researchers and techno heads areRequest Solution Demo constantly striving to beat the levels achieved by previous AIs? Here are some of the top artificial intelligence tech trends this year.

Deep learning

Deep neural networks have the ability to mimic human brain by learning from images, audio, and text data. They have been in use for more than a decade, however, there’s a lot still to be discovered and learn how a neural network learns and how can they be made efficient. Deep learning is getting smarter though, instead of feeding hundreds and thousands of data points, today’s systems can give an accurate output by factoring only few hundred data points.

Deep reinforcement learning

You learn from your own mistakes is probably the most realistic statements ever made. The developers of artificial intelligence have better understood this and are applying this principle of reinforced learning to their systems. This is the exact reason why the famous AlphaGo was able to beat a human champion. More recently, it beat a DOTA champion in a very complex game by teaching itself to play the game within two weeks. Researchers are relying more on this technique as it uses fewer data to train its models.

Augmented data learning

Machine learning works by accumulating a wide variety of data sources to train the system. However, unavailability of such kind of data poses a big problem for artificial intelligence systems. Emerging tech trends use new synthetic data and transfer a model trained for one domain to be used in another. Transfer learning or one-shot learning techniques are being used currently to teach AI systems without significant data sources. Similarly, it can address a wider variety of problems, which has less historical data.

Hybrid learning models

Machine learning takes in and processes data with a fixed set of rule and depends on metadata to have information in a certain format. However, they do not have a model for uncertainties like the way probabilistic or Bayesian approaches do. New hybrid learning models combine the two approaches to leverage the strengths of each one of them. Hybrid learning helps in solving various business problems with deep learning by factoring in uncertainty. Bayesian GANs, Bayesian deep learning, and Bayesian conditional GANs are some of the examples of such hybrid learning models.

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ir10

Machine Learning Revolutionizing the Manufacturing Machine

Artificial intelligence and machine learning have been making headlines for topics like driverless cars, predictive texts, personal assistant, and gaming. Although not much discussed, machine learning has been bringing about landmark changes in the area of industrial manufacturing. The manufacturing industry is always on the lookout to improve productivity and efficiency. They want to go beyond the gains achieved by automation and build on top of that. Machine learning’s core technologies do align seamlessly with complex manufacturing problems. Numerous algorithms are being developed which seek to learn continually and bring about optimizations in the manufacturingFree demo plant. So how will machine learning revolutionize the manufacturing industry?

Increased production capacity and decreased material consumption

Smart manufacturing systems make use of machine data including production capacity, daily production, and machine load. Such data is essential to perform a predictive data analysis, which uses machine learning to know the ideal production capacity and decrease the rate of material consumption.

Predictive maintenance

Today, more and more companies are integrating their machine learning databases, apps, and algorithms into the cloud platform. Data generated from industrial equipment can be fed to predictive algorithms, which will then be assessed by remote experts. Such data is essential to generate predictive models to alert the local technician to perform a precautionary check and prevent any sudden machine downtime.

Improved supply chain management

The modern supply chain generates a massive amount of data. It is humanly impossible to analyze such data, so experts turn towards AI and machine learning to organize it into useful bits and pieces. AI and machine learning make it easier for companies to adapt to dynamic market conditions. As a result, many vendors have been providing cloud-based supply chain management solutions, which collect and analyze data from different sources, including weather forecasts, historical data, news feed, and social media. Such data can be processed to enhance performance in all areas of supply chain including transportation, warehousing, packaging, customer feedback, and production.

Focus on customers

The customer is the king. It comes as no surprise that companies are aligning their business processes to address the needs of the customer. AI and machine learning can deliver exactly what people demand; thus, allowing for a high level of customization. Such technologies allow companies to build smart manufacturing processes that can seamlessly adapt to changing customer demands. Additionally, companies can collect data from smart homes to understand the latest consumer trends and design products accordingly.

Quality control

Players across the manufacturing industry are pouring big chunks of money into machine-learning based quality assurance. Till today, companies rely on sensors and computers to remove low-quality products from their assembly line. Machine learning will provide a seamless quality control over the entire manufacturing process. Manufacturers can easily identify defects with efficiency and accuracy, and thus optimize its production and maintain customer satisfaction.

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IR15

Top AI Applications That will Revolutionize the Banking Sector

It’s just impressive how the human brain learns new information and uses it to solve problems. Such behavior is evident even in children as they learn from their environment and have their own way of interpreting things. Computers have been trying hard to emulate such intelligence and today, they are getting close. Such AI applications can positively impact our lives and are already present in some form around us. For instance, one of the most common AI applications can be seen in the form of Apple’s voice assistant Siri or Netflix’s content recommendation engine. These AI systems can learn from your behavior and assist you in solving your queries. At some level, AI integration is already a crucial part of our lives. Just imagine a system which can think the way as humans do, but perform millions of calculations per second. The amount of problems they can solve can be limitless. And it comes as no surprise that companies such as AIBrain, Anki, Banjo, and iCarbonX are investing heavily in this technology to Free demoprovide solutions in the banking sector. So what are the key AI applications that benefits the banking sector?

AML pattern detection

Anti-money laundering (AML) activities have been troubling governments all across the world. To crack down such activities, it is essential to think like them and develop countermeasures to tackle such issues. People involved in AML operate skillfully by depositing small sums of money which don’t trigger bank reporting requirements. AML detection is one of the most prominent AI applications in the banking sector as it can assign AML threat score to detect such fraud. By profiling each customers banking transactions, AI systems can learn users banking behavior and detect anomalies to crack down on AML activities.

Chatbots

Customer support is one of the crucial areas that ensures customer satisfaction in the banking sector. However, most of the customer queries are repetitive in nature, and it costs banks a lot to serve such customers. Uncontrolled spending can increase banks operations expenses and subsequently increase their cost of service. Chatbots are a welcome addition to this list of AI applications in the banking sector as it can simulate human chats. Such systems identify the content and emotions in the message and give an appropriate reply or even solve customer queries. After engaging in a lot of real-time chats, the system can learn to provide more accurate responses through machine learning. Bank of America, JPMorgan Chase, Capital One, Master Card, and many more banking companies are already using this technology to improve customer satisfaction.

Algorithmic trading

Many people make millions of dollars in micro trading. Such people deal with a single stock and buy and sell shares multiple times within a single day. Such trading requires people to process vast amounts of information. AI systems can replicate such transactions by making accurate calculations to trade and turn in a profit. Numerous reports state that 70% of the trading today is carried out by automated AI systems.

Fraud detection

One of the key AI applications in the banking sector has been in the area of fraud detection. It has done so with superior results. AI systems process multiple inputs and learn from user behavior to distinguish regular transaction from that of fraudulent ones. Although fraud attacks have become more sophisticated, AI and machine learning systems have always been in the driver’s seat. AI and machine learning skims through all transaction data to identify visible patterns and detect anomalies to identify fraud. Even the early data analysis technique FICO Falcon fraud assessment system has been refined to tackle modern-day fraud.

Customer recommendation

Similar to how content recommendation engine works seamlessly to suggest you the music or video you like, players in the banking sector can use customer recommendation. Using past data on users and how they behaved towards various offers and promotions from the bank, AI systems can recommend suitable users based on their history with the bank. Such customer recommendation systems help banks to grow their revenue stream and match customers to particular products.

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IR36

What Happens When a Computer Can Perceive the World as Human’s do? Enter Computer Vision

iPhone X’s Face ID was greeted with a lot of hype when it was announced. The facial recognition technology has the capabilities to differentiate the owner of the device from others just by scanning the face. In the same year, Google Pixel had an inbuilt app called Google Lens, which could scan images and return relevant and accurate results. So how exactly do computers know what it’s looking at? Much of this can be attributed to computer vision, which utilizes neural networks, machine learning, and image recognition to make accurate human-like decisions regarding images. However, knowing what the picture is can be impressive, but what significance does it offer in changing the Free demotechnological landscape?

Applications of computer vision and machine learning algorithms

Self-driving cars

It may be possible to direct the self-driving cars to drive in a particular lane or apply brakes when an obstacle appears in their proximity. But one major challenge for autonomous vehicles is its ability to identify road signs, lane markings, traffic signals, and hand signals. Advanced computer vision and machine learning algorithms can quickly detect whether the obstacle nearby is a boy crossing the road or just some object which can be circumvented. Additionally, it can also understand fellow drivers’ hand signals and make necessary adjustments.

Image search

Computer vision enables users to understand the content of the image by using powerful artificial intelligence tools, which can quickly categorize images across thousands of categories. These image searches are so powerful that it can list out all the objects within a single picture and also identify objects shot through multiple angles. Image search can help companies identify explicit or offensive content, so they can block them before they create a nuisance. Google Cloud Vision API and Microsoft Azure Computer Vision API are two of the most significant players in this area.

Online merchandising

Today, the recommended products section is primarily driven by simple algorithms, which classify product type and display other goods that are from the same category. However, with advancements in computer vision and machine learning algorithms, retail companies can now show products that have a similar design, color, or style. Visual product discovery can recommend a large number of products and find similar goods in a separate category. For instance, a customer may like a sneaker which might have been placed in the sports shoe category. This way, artificial intelligence tools can enhance customer experience.

Emotional analytics

People may be impressed by how accurately the new iPhone X can detect faces. However, that is just the tip of the iceberg in terms of computer vision. Computers are far more capable than just recognizing faces. They can identify and decipher facial expressions and ascertain whether the person is feeling happy, sad, or perplexed. Such technologies are mostly used in content testing, where advertisers capture the reaction of the audience to their ads.

Gesture recognition

Understanding non-verbal cues, body language, and gestures is a crucial aspect of human behavior, and that is what sets it apart from machines. However, the line between humans and machine regarding this aspect is blurring. Advancements in computer vision and artificial intelligence means that computers can understand human gestures. Such technology is embraced by companies such as PointGrab, which offers home appliance with smart gesture recognition technology, allowing users to control the appliances by using hand gestures.

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Telecom Industry Client leverages Salesforce Analytics to Promote Sales and Business Development

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LONDON: Quantzig, a global analytics services provider, has recently completed their latest salesforce analytics for a telecom industry client. The growth of the internet over the past couple of decades has boosted the growth of this industry. The telecom industry is primarily driven by technological innovations and developments that offer a wide range of facilities at low-cost margins.

“Salesforce analytics helps firms in the telecom industry to offer personalized experiences to the customers. Also, with the help of salesforce analytics solution, businesses can better engage with customers and improve their customer loyalty.” says an industry expert from Quantzig.

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The salesforce analytics solution offered helped the client to address the real-time needs of the customers based on their expectations and provide a convenient user experience to improve customer loyalty. Furthermore, the solution also helped the client to efficiently manage their workflows and processes resulting in increased efficiency, transparency, and profitability.

Additional Benefits of Salesforce Analytics

  • Better understand the customers and make quick data-driven actions.
  • Comprehend customer behavior and reach out to them seamlessly.
  • To know more, request a free proposal

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IR13

Retail Revolution and the Power of AI

The use of AI and machine learning in the retail industry is growing at an exponential rate. Retailers have been able to see the results delivered by artificial intelligence systems instantly. AI is expected to become pervasive across customer journeys, merchandising, marketing, and supply networks as it can provide detailed insights to optimize the retail operations. Big data and machine learning have been successfully used by several retailers to achieve substantial increase in their operating margins. Such technologies can enable retailers to deliver personalizedFree demo experiences to the customer in order to increase loyalty and spending. There are various use cases for AI in retail industry that can change the way this sector operates.

Uses of AI in retail industry

Sales and CRM applications

In 2010, Japan’s SoftBank telecom developed a humanoid robot, Pepper, which can interact with customers and perceive human emotions. The robot was used as a customer service greeter and representative in over 140 stores. The company later reported up to 70% increase in footfall in multiple stores. Additionally, an American company developed AI-powered sales-assistant software, Conversica, which identifies and converses with internet leads to enhance sales. The customized sales assistant software is also used for cross-selling and re-engaging existing leads.

Customer Recommendation

Product recommendation tools are adding significant revenues to e-commerce businesses. IBM Watson is one of the most advanced AI systems that exhibits order management and customer engagement capabilities. IBM Watson uses personality insights taking into account users personal information, browsing history, past transaction data, and other dynamic data including weather, location, time, and items in cart to develop its recommendation engine. By calculating respective personality profile, IBM Watson can accurately suggest brands and products users are most likely to buy. For instance, North Face has used IBM Watson’s cognitive computing technology to suggest jackets for the customers based on variables like gender and location.

Manufacturing

Long after automation revolutionized the manufacturing industry, AI is set to be next wave of change in this sector. AI can help companies keep inventories lean and reduce the cost. For instance, GE’s Brilliant Manufacturing software enables manufacturers to predict, adapt, and react more effectively by incorporating SCADA, MES, and analytics. It empowers decision makers with deep operational intelligence and real-time visibility to reduce unplanned downtime and inventory.

Logistics and delivery

Domino’s Robotic Unit (DRU) has developed a prototype delivery robot that can keep food and drinks at an appropriate temperature. Its sensor also helps the device to navigate the best travel path for delivery. Alongside DRU, Amazon’s Prime Air is expected to be the future of delivery systems. Such drones can deliver parcels up to five pounds in weight in less than 30 minutes. The autonomous delivery of goods can significantly improve the performance of the retail industry and increase customer satisfaction.

Payment services

With a view to reign in the retail industry, Amazon introduced its brick-and-mortar store, Amazon Go which enables check-out free technology that allows customers to shop and leave. Their check-out free shopping experience uses the same kind of AI technology used in autonomous cars including computer vision, sensor fusion, and deep learning. It automatically detects when the products are taken from or returned to shelf keeping track of it in a virtual cart. When customers are done with their shopping, they simply check out of the store, and Amazon will deduct the amount from their Amazon account. With regards to payment services, AI is also showing its potential in payments fraud. Payment fraud is a matter of concern in the e-commerce space, where fraudsters are using stolen accounts to make purchases. AI technologies can study thousands of purchase patterns to differentiate between payment made by the genuine user and a fraudster.

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Five Interesting Applications of AI in the Food Industry You May Not Have Known

AI and machine learning have been the buzzword for quite some time now. The use of such tools was previously limited to the digital world and computer science. However, recently it has made its way into multiple sectors including healthcare, education, finance, marketing, media, transportation, and gaming. Similarly, the food industry is also catching up to the developments in AI. From cooking to detecting food freshness, artificial intelligence has a lot of applications in the food industry. It can help the players in the F&B industry to improve offerings, deliver a better customer experience, and optimize operations. The growing use of AI in the food and beverage industry can be attributed to increasing customer expectations for foods that are healthy, unique, and hanRequest Solution Demodmade. Also, food manufacturers are constantly battling it out to find the perfect mix of taste, appearance, healthiness, and cost. That’s the exact reason why food companies are investing heavily in raw materials, safety and quality controls, and storage and distribution.

AI in the Food Industry

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IR22

How Is AI Technology Going to Turn Tables for The Banking Sector?

Artificial Intelligence (AI) is no new term in the banking sector; in fact, many financial institutions have already found success with the help of AI. But how can this technology be leveraged for banking functions, you wonder? Artificial intelligence helps improve customer personalization, identify connections, and patterns that cannot be quickly figured out by humans and provides answers to several banking issues in real-time. But that is not all, here are some of the few other benefits that AI promises to offer to the banking sector:

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