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Preparing Your Business For The Artificial Intelligence Revolution

Today, when asked about artificial intelligence (AI), many people start painting science fiction inspired images of machine-ruled futures and robots completing manual tasks for human beings. To them, AI is only a concept, something that’s going to happen tomorrow. In reality, artificial intelligence is already part of our lives. We use AI every day. It’s not only on your smartphones, laptops and cars, it’s everywhere.

Is 2018 The Year That The AI Revolution Goes Mainstream?

For the last few years, AI has entered the consciousness of every industry. It has become part of mainstream conversations. Businesses of all shapes and sizes are considering artificial intelligence to solve real business problems.

In the past, only the largest corporations could afford to invest in AI technology, but things are changing fast. In fact, the high-speed growth of AI makes it more likely that startups and younger businesses will be able to embrace the technology earlier than their corporate colleagues.

According to a PricewaterhouseCoopers (PwC) report, the global economy can see a potential contribution of $15.7 trillion from AI by the year 2030. China and North America will receive almost 70% of this potential global GDP growth.

How businesses can benefit from artificial intelligence

Many are calling artificial intelligence (AI) technologies’ relatively recent boom software’s second coming. Yet, despite major developments since the AI winter of the late 21st century, AI technologies are still struggling to break into business processes. Here are a few reasons why artificial intelligence should be adopted into the workplace.

Imagine the logistics behind a major international airline like Emirates. They’re interacting with literally hundreds of thousands of people every day – across social media and the real time market. Sheer volume makes customer relationship management critical to their business model.

And that’s where artificial intelligence can step in. Chatbots and intelligent systems can be Emirates’ always on connection to their markets. Should a customer’s flight be delayed, there can be systems in place that connect with said customer, keeping them updated on schedules, and referring them to new information as it happens, with personalized notifications. Chatbots can respond to customer queries, regardless of what time it is. This can only have a positive impact on the overall brand momentum.

How businesses can benefit from artificial intelligence

Machine learning (ML) extracts meaningful insights from raw data to quickly solve complex, data-rich business problems. ML algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so. ML is evolving at such a rapid rate and is mainly being driven by new computing technologies.

Machine learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics community. Factors such as growing volumes, easy availability of data, cheaper and faster computational processing, and affordable data storage have led to a massive machine learning boom. Therefore, organizations can now benefit by understanding how businesses can use machine learning and implement the same in their own processes.

10 Business Benefits of Machine Learning ML helps in extracting meaningful information from a huge set of raw data. If implemented in the right manner, ML can serve as a solution to a variety of business complexities problems, and predict complex customer behaviors. We have also seen some of the major technology giants, such as Google, Amazon, Microsoft, etc., coming up with their Cloud Machine Learning platforms. Some of the key ways in which ML can help your business are listed here -

Customer Lifetime Value Prediction Customer lifetime value prediction and customer segmentation are some of the major challenges faced by the marketers today. Companies have access to huge amount of data, which can be effectively used to derive meaningful business insights. ML and data mining can help businesses predict customer behaviors, purchasing patterns, and help in sending best possible offers to individual customers, based on their browsing and purchase histories.

Predictive Maintenance Manufacturing firms regularly follow preventive and corrective maintenance practices, which are often expensive and inefficient. However, with the advent of ML, companies in this sector can make use of ML to discover meaningful insights and patterns hidden in their factory data. This is known as predictive maintenance and it helps in reducing the risks associated with unexpected failures and eliminates unnecessary expenses. ML architecture can be built using historical data, workflow visualization tool, flexible analysis environment, and the feedback loop.

Eliminates Manual Data Entry Duplicate and inaccurate data are some of the biggest problems faced by THE businesses today. Predictive modeling algorithms and ML can significantly avoid any errors caused by manual data entry. ML programs make these processes better by using the discovered data. Therefore, the employees can utilize the same time for carrying out tasks that add value to the business.

Detecting Spam Machine learning in detecting spam has been in use for quite some time. Previously, email service providers made use of pre-existing, rule-based techniques to filter out spam. However, spam filters are now creating new rules by using neural networks detect spam and phishing messages.

Product Recommendations Unsupervised learning helps in developing product-based recommendation systems. Most of the e-commerce websites today are making use of machine learning for making product recommendations. Here, the ML algorithms use customer's purchase history and match it with the large product inventory to identify hidden patterns and group similar products together. These products are then suggested to customers, thereby motivating product purchase.

Financial Analysis With large volumes of quantitative and accurate historical data, ML can now be used in financial analysis. ML is already being used in finance for portfolio management, algorithmic trading, loan underwriting, and fraud detection. However, future applications of ML in finance will include Chatbots and other conversational interfaces for security, customer service, and sentiment analysis.

Image Recognition Also, known as computer vision, image recognition has the capability to produce numeric and symbolic information from images and other high-dimensional data. It involves data mining, ML, pattern recognition, and database knowledge discovery. ML in image recognition is an important aspect and is used by companies in different industries including healthcare, automobiles, etc.

Medical Diagnosis ML in medical diagnosis has helped several healthcare organizations to improve the patient's health and reduce healthcare costs, using superior diagnostic tools and effective treatment plans. It is now used in healthcare to make almost perfect diagnosis, predict readmissions, recommend medicines, and identify high-risk patients. These predictions and insights are drawn using patient records and data sets along with the symptoms exhibited by the patient.

Improving Cyber Security ML can be used to increase the security of an organization as cyber security is one of the major problems solved by machine learning. Here, Ml allows new-generation providers to build newer technologies, which quickly and effectively detect unknown threats.

Increasing Customer Satisfaction ML can help in improving customer loyalty and also ensure superior customer experience. This is achieved by using the previous call records for analyzing the customer behavior and based on that the client requirement will be correctly assigned to the most suitable customer service executive. This drastically reduces the cost and the amount of time invested in managing customer relationship. For this reason, major organizations use predictive algorithms to provide their customers with suggestions of products they enjoy.

How businesses can benefit from artificial intelligence

One of the hottest buzzwords of this decade is Facial Recognition.

It’s the part of applied machine learning that can detect and identify human faces, a problem that has been notoriously difficult for computers up to now. And with this has burst open a whole new world of exciting possibilities and challenges for businesses, governments, and individuals alike.

If you’re a business leader and have been wondering what the fuss is all about, and whether there’s some utility in this new development, we’ve got you covered. In this article, we’ll look at the history of Facial Recognition, its development, current uses, controversies, deployment, and many more facets. By the end of it, you’ll have a solid grasp of what the Facial Recognition technology is all about, and what its implications are for businesses.

What is visible Surface detection?

When a picture that contains the non-transparent objects and surfaces are viewed, the objects that are behind the objects that are closer cannot be viewed. To obtain a realistic screen image, these hidden surfaces need to be removed. This process of identification and removal of these surfaces is known as Hidden-surface problem. The hidden surface problems can be solved by two methods − Object-Space method and Image-space method. In physical coordinate system, object-space method is implemented and in case of screen coordinate system, image-space method is implemented. When a 3D object need to be displayed on the 2D screen, the parts of the screen that are visible from the chosen viewing position is identified. In reality, artificial intelligence is already part of our lives. We use AI every day. It’s not only on your smartphones, laptops and cars, it’s everywhere.

Deep learning for biology

Four years ago, scientists from Google showed up on neuroscientist Steve Finkbeiner's doorstep. The researchers were based at Google Accelerated Science, a research division in Mountain View, California, that aims to use Google technologies to speed scientific discovery. They were interested in applying ‘deep-learning’ approaches to the mountains of imaging data generated by Finkbeiner’s team at the Gladstone Institute of Neurological Disease in San Francisco, also in California.
Deep-learning algorithms take raw features from an extremely large, annotated data set, such as a collection of images or genomes, and use them to create a predictive tool based on patterns buried inside. Once trained, the algorithms can apply that training to analyse other data, sometimes from wildly different sources.
The technique can be used to “tackle really hard, tough, complicated problems, and be able to see structure in data — amounts of data that are just too big and too complex for the human brain to comprehend”, Finkbeiner says.
He and his team produce reams of data using a high-throughput imaging strategy known as robotic microscopy, which they had developed for studying brain cells. But the team couldn’t analyse its data at the speed it acquired them, so Finkbeiner welcomed the opportunity to collaborate.
“I can’t honestly say at the time that I had a clear grasp of what questions might be addressed with deep learning, but I knew that we were generating data at about twice to three times the rate we could analyse it,” he says.
Today, those efforts are beginning to pay off. Finkbeiner’s team, with scientists at Google, trained a deep algorithm with two sets of cells, one artificially labelled to highlight features that scientists can’t normally see, the other unlabelled. When they later exposed the algorithm to images of unlabelled cells that it had never seen before, Finkbeiner says, “it was astonishingly good at predicting what the labels should be for those images”. A publication detailing that work is now in the press.
Finkbeiner’s success highlights how deep learning, one of the most promising branches of artificial intelligence (AI), is making inroads in biology. The algorithms are already infiltrating modern life in smartphones, smart speakers and self-driving cars. In biology, deep-learning algorithms dive into data in ways that humans can’t, detecting features that might otherwise be impossible to catch. Researchers are using the algorithms to classify cellular images, make genomic connections, advance drug discovery and even find links across different data types, from genomics and imaging to electronic medical records.
More than 440 articles on the bioRxiv preprint server discuss deep learning; PubMed lists more than 700 references in 2017. And the tools are on the cusp of becoming widely available to biologists and clinical researchers. But researchers face challenges in understanding just what these algorithms are doing, and ensuring that they don’t lead users astray.
Training smart algorithms
Deep-learning algorithms (see ‘Deep thoughts’) rely on neural networks, a computational model first proposed in the 1940s, in which layers of neuron-like nodes mimic how human brains analyse information. Until about five years ago, machine-learning algorithms based on neural networks relied on researchers to process the raw information into a more meaningful form before feeding it into the computational models, says Casey Greene, a computational biologist at the University of Pennsylvania in Philadelphia. But the explosion in the size of data sets — from sources such as smartphone snapshots or large-scale genomic sequencing — and algorithmic innovations have now made it possible for humans to take a step back. This advance in machine learning — the ‘deep’ part — forces the computers, not their human programmers, to find the meaningful relationships embedded in pixels and bases. And as the layers in the neural network filter and sort information, they also communicate with each other, allowing each layer to refine the output from the previous one.