Artificial Intelligence – Page 12

10 Amazing Advantages of Machine Learning You Should Be Aware Of!

Machine learning (ML) extracts concrete lessons from raw data to solve complex, data-rich business problems fast. ML algorithms iteratively learn from the data and enable computers to discover various types of deep insights without being specially trained to do so. ML develops at such a rapid rate and is driven primarily by emerging computational technology. Machine learning in business helps improve business scalability and business operations for companies around the globe. In the business analytics community, artificial intelligence tools and numerous ML algorithms have gained tremendous popularity. Factors including rising quantities, convenient data access, simpler and quicker computer capacity, and inexpensive data storage have led to a massive boom in machine learning. Organizations can, therefore, profit from knowing how businesses can use machine learning to apply the same in their processes. Machine learning (ML) and Artificial Intelligence in the business sector have created a lot of hype. Marketers and business analysts are curious to learn about the advantages of machine learning in the industry and its implementations. For ML architectures and Artificial Intelligence, several people have heard for. But they’re not entirely conscious of it and its implementations. You must be mindful of the business problems it can address to use the ML in the market. Machine learning collects useful raw data knowledge and offers detailed tests. And that knowledge helps to solve dynamic and data-rich issues. Machine learning algorithms, too, learn and process from the input. The methodology is used without needing to be trained to find different perspectives. The ML is rapidly evolving and being powered by new technologies. It also allows the company to boost regional organization scalability and business operations. Recently, in their company, several top-ranking businesses such as Google, Amazon, and Microsoft have embraced machine learning. And they’ve introduced tools for online machine learning. Why Machine Learning Is Important? Machine learning is important because it primarily works with a huge variety of data. Processing big data is cheaper when you use an algorithm to automate the process rather than rely on manual processes done by humans. A machine learning algorithm can be quickly trained to analyze datasets and detect patterns that are not easily identifiable otherwise. ML makes automation possible, which, in turn, saves time, money, and resources for an enterprise. When you can get better and more accurate results for a fraction of a cost and in a handful of minutes, why not invest in machine learning models? Here’s why machine learning is important in today’s world: Voice assistants use Natural Language Processing (NLP) to recognize speech and convert it into numbers using machine learning. The voice assistant then responds appropriately. While Google Assistant, Siri, etc., are used in domestic life, organizations are using similar voice assistants at the workplace to help employees interact with machines using their voices. It promotes self-service and allows employees to rely on technology instead of their colleagues to finish a task. Companies in the transportation industry (like Ola, Uber, etc.) use machine learning to optimize their transportation services. Planning the best route, setting up dynamic pricing based on the traffic conditions, and other such aspects are managed using machine learning software. ML also helps create better physical security systems to detect intruders, prevent fake alarms, and manage human screening in large gatherings Machine learning helps improve the quality of output by minimizing/ preventing bottlenecks. Be it the production lifecycle, cyber security, fraud detection, risk mitigation, or data analytics, ML technology offers valuable insights in real-time and gives businesses an edge over the competitors. Some Basic Advantages of Machine Learning Here are some of the major benefits of machine learning that every businessman must be aware of. Each business organization relies on the information received through data analysis. Big data is on the businesses. But it’s difficult to extract the right information and make a decision from the results. Machine learning takes advantage of ML algorithms. It also learns from data already in use. The findings help to make the right decision for the businesses. It allows companies to turn data into knowledge and intelligence that can be used. The experience will work into daily business processes. These processes then deal with changes in market requirements and business circumstances. Business organizations should use machine learning in this way. It holds them on top of the rivals. Top Advantages Of Machine Learning ML aims to derive meaningful information from an immense amount of raw data. If implemented correctly, ML can act as a remedy to a variety of problems of market challenges and anticipate complicated consumer behaviors. We’ve already seen some of the significant technology companies coming up with their Cloud Machine Learning solutions, such as Google, Amazon, Microsoft, etc. Here are some of the critical ways ML can support your company: 1. Customer Lifetime Value Prediction Prediction of consumer lifetime value and segmentation of consumers are some of the significant challenges the advertisers face today. The business has exposure to vast amounts of data, which can be used easily to provide insightful insights into the Market. ML and data mining will help companies to forecast consumer habits, purchasing trends, and improve individual customers to submit best-possible deals based on their surfing and purchase experience. 2. Predictive Maintenance Manufacturing companies regularly follow patterns of preventive and corrective repair, which are often costly and ineffective. With the emergence of ML, though, businesses in this field will make use of ML to uncover valuable observations and trends embedded in their data on their factories. It is recognized as predictive maintenance, which helps reduce the risks of unforeseen problems and reduces needless expenditures. Historical data, workflow visualization tools, flexible analytical environments, and feedback loops can be used to build ML architecture. 3. Eliminates Manual Data Entry Duplicate and unreliable records are among the most significant problems. The businesses are facing today. Machine Learning algorithms and predictive models will significantly prevent any errors induced by manual data entry. ML programs use the discovered data to make these processes better. The employees can, therefore,

AI in Pharma: How Pharma Industry is Getting Smarter Today

Artificial Intelligence or AI in the pharma industry presents various opportunities to substantially improve the pace of drug discovery and distribution process. The current protocol followed needs to be upgraded in order to meet the rising demand for medicine and that too without compromising its quality. Advanced AI solutions will help pharma companies to process structured and unstructured data in order to derive useful and actionable insights. The application of machine learning and AI to drug discovery will not only accelerate the process but also help companies to spawn a higher return on investment. It will make it easier for scientists to find potential targets and for the manufacturers to ensure its timely delivery. McKinsey estimates that machine learning and big data can help to generate a profit of around $ 100 billion for the pharma industry. The insights produced with the help of analytics would help the pharma companies to make better decisions, improve the efficiency of clinical trials, advance the shipping process and ultimately achieve greater commercial success. What is Artificial Intelligence in Pharmaceutical Industry? AI in the pharma industry is the use of algorithms, computer vision technologies, and automation to speed up tasks that were traditionally performed by humans. The pharma and biotech industry saw huge investments in artificial intelligence in recent times. From market research to drug development and cost management, AI is playing a vital role in modernizing the pharma industry and bringing new drugs faster into the market. Big data and AI-based advanced analytics have brought a radical change in the pharma sector. Faster innovation, increase in productivity, and building comprehensive supply chain systems are possible with artificial intelligence. According to a study conducted by the Massachusetts Institute of Technology (MIT), less than 14% of the new drugs pass clinical trials. Moreover, the pharma company has to pay billions to get the drug approved by the government authorities. By using artificial intelligence in pharmaceutical research and development, pharma companies can increase their success rate. The data from clinical trials are collected and processed using AI and ML systems to derive insights about the drug and its reactions to the test subjects. The positives and side effects are carefully observed and analyzed to make the necessary changes to the drug’s composition. This will result in drugs with a better curing capacity and fewer side effects. The pharma industry requires billions to keep up the R&D. The company spends huge amounts of money at every stage to ensure that the drug is made using quality materials and in hygienic and sterile conditions. The warehouse for storing inventory should have a temperature control facility to maintain the necessary conditions for the drugs to retain their original composition. By adopting artificial intelligence software apps and integrating them with systems in the pharma company, the management can streamline the process from start to finish. This will reduce operational costs and minimize the risk of damaging the drugs. Let’s take Novartis as an example. The pharma company is investing in AI and ML to find ways to speed up the treatment processes and help patients become healthier. The company is working on classifying digital images of cells based on how they are responding to treatment (compounds). The ML algorithms collect the research data and group cells with similar responses to the compounds used for the treatment. This information is then shared with the research team to help them use the insights and their experience in understanding the results. Novartis uses the images developed by machine learning algorithms to run predictive analytics and identify cells that may not respond to the treatment. The ML algorithms make it easier to study large amounts of data and identify the patterns of different diseases, their impact on the cells and organs, the symptoms, and the possible treatment methods/ drugs that can cure the diseases. A pharma company that invests in adopting artificial intelligence at each level (R&D, production, supply chain, etc.) will have an edge over competitors and can provide expensive drugs for cost-effective prices to make treatment affordable for more patients. AI in Pharma Industry: The Transformation Look how ML and AI models are transforming the pharma industry and making it even better than before. Supply Chain Management Optimization of the supply chain across pharmaceutical industries has always been a challenge for the owners. However, with the advent of AI and ML, the process is becoming smoother. The big data generated helps companies to reach out to their prospective clients and understand their needs, which in turn ensures the number of drugs to be produced by the companies. Also, predictive analytics insights generated with the help of big data allow the companies to foresee the demand pattern and hence manufacture only the required quantity of medicines. The drugs today are being increasingly customized for even small populations with particular genetic profiles. Finding out a way to deliver a medicine that is relevant only to a small bunch of 1000 people is more difficult than delivering medicines across the world. This venture requires proper utilization of resources so that there is no delay in delivery and loss to the company. An expert at “LogiPharmaUS Conference” in 2017 said that “Instead of executing one supply chain a thousand times, we should get ready to execute a thousand supply chains, one at a time.” This act will not only ensure timely drug delivery but also safeguard the hassle of re-execution every time. Machine learning and AI algorithms can help to automate this process and make it more robust. Now, when we talk only about shipping drugs, there are many medicines that are expensive and require very specific conditions to be transported. Billions and trillions of money are spent by the pharma companies to deal with the transportation process. With the application of ML and AI pharma, companies will be able to forecast demand and distribute products efficiently. Also, many key decisions will become automated allowing the companies to cut down their labor costs and make more profit.

11 Insane Machine Learning Myths Debunked for You!

The world is becoming smart, smarter than ever before. There are homes that know how to turn on the lights by judging their intensity and there are cars that can drive themselves. Isn’t it something like living in a sci-fi world? Everything that was imagined is turning into reality. Among all that we hear about the upcoming technology, machine learning (ML) is a common term being associated with almost all of them. The term has been more misinterpreted than understood and there has been a considerable measure of hype buzzing around it. With more gadgets and technologies being launched every day, customers are keen to know what is it that is making them smarter? They are curious to discern the tech running behind the smartness and understand how it can benefit them in their personal as well as business ventures. This inquisitiveness towards the “working” has lured people to read and question about the same, however, the responses have not been palatable. For instance, you may often see mobile companies using the terms artificial intelligence and machine learning interchangeably for their products, now this is how a misperception is shaped. The customers do not understand the difference between the two and start treating them as synonymous with each other. The aim here is to make you understand the similarities and differences between “machine learning” and the terms it is confused with. this write-up shall provide you with a clear insight so that you can differentiate between the hype and the reality. It is important because machine learning forms an integral part of almost all data-driven work. In the event that you intend to consolidate it into your business, you should discern what it may or may not be able to do for you. Having a clear perspective will ensure that you develop a strategy that fits into your business module and helps you accomplish the set objectives. Removing the Misconception You know how they say in school that if your basics are clear, you will understand each and every concept and if not then surely there will be trouble. This concept will hold true in your entire life and therefore if you recognize the simple notion of machine learning you’ll never be influenced by the related hysterias. The figure below describes machine learning in its most naive form. There is a lot of reality and there is a lot of hype pertaining to machine learning. But with the above-illustrated diagram, it should be clear that machine learning is, training a machine by giving it a large amount of data and then letting it perform based on that learning. Exposing the Machine Learning Myths Machine learning is currently going through a phase of inflated expectations. Along with ongoing machine learning developments around the globe , there are still a lot of organizations looking forward to conceptualizing and running ML projects without even exploring the power of basic analytics. How do you expect them to meet their goals when they do not know what ML can or cannot do? In such a scenario it becomes imperative to know the myths and truths related to the subject. #1 Machine Learning and Artificial Intelligence Are Same One of the most common misconceptions is between artificial intelligence and machine learning. Both the terms are not only different in words but are two different fields belonging to a bigger pool of data science. In order to understand the difference consider this example – You wish that the camera of your phone should recognize a dog. Now in order to do that you provide it with a huge amount of data that contains pictures of all the types of dogs present in the world. With the help of these images, the camera is able to create a pattern that resembles a dog. Now whenever you point the camera toward the dog, it matches the pattern and that is how you get a positive hit. On the other hand, pointing the camera toward a cat doesn’t identify it as anything. This is a machine-learning process where the machine is trained to accomplish a particular task. Artificial Intelligence on the other hand is a broader concept, where the machines are trained in such a way that they can make their own decisions just like the human brain. If you put a cat in front of a camera that works on an AI technology, it will use it as another input and further reuse it to train itself. This training would help the AI-enabled phone to tell that isn’t a dog but it may be something else that can be explored. #2 Hiring the Best ML Talent Is Sufficient to Resolve Business Issues Business firms are spending a lot of money in gathering the best machine learning talent which can analyze their data and offer useful insights. What they forget in the process is that machine learning is just one part of an effective strategy, the basics are to have the right type and amount of data. If there is no one who can fetch the data, what will the professionals work upon? Therefore, businesses do not need a staff good in one field but someone who knows how to work from the scratch. There are data science firms all over the globe that can help businesses develop a correct approach and provide the useful insights they have been looking for. #3 ML Implementation Requires Humongous Infrastructure Machine learning sounds scientific and complicated that many presume it is not meant for their business. After all, what will an ordinary business do with advanced technology? Not every SME hires AI experts, isn’t it? That’s where we are wrong Years before it was said that if you wish to carry our ML operations on your premises, you’ll need to invest a large amount in infrastructure. The scenarios have changed now. Since data science and data analytics has become such an integral part of the business world, there are professionals who are

6 Innovative Ways of Using Machine Learning in E-Commerce

Machine learning is one of the most searched keyword on any search engine at this point of time. The reason is quite clear; the benefits of utilising it in any industry is beyond imagination. We are explaining how an e-commerce business can make use of machine learning for profit maximisation

Machine Learning for Transactional Analytics: Customer Lifetime Value v/s Acquisition Cost

Understanding customer transactional behavior pays well for any business. With the tsunami of start-ups in recent times and the immense money flow in businesses, customers find lucrative offers from companies for acquisition, retention & referral strategies. Understanding the transactional behavior of a customer has become even more complex with the advent of new business houses every day. Although with the rise of powerful machines, one can easily manage to work with TBs of data, the complexity of business economics has made this behavioral analysis far more difficult. Collecting and analyzing your business data on all aspects such as acquisition cost, operational cost, base profit, revenue growth, referrals, etc can help in providing the lifecycle profit patterns from a customer. But it does not help in solving many business questions such as: What is the actual value of a new customer in Dollars worth today? How much money business can spend to acquire a new customer? Let’s take an example to understand it more intuitively. Firstly, to estimate the value of a new customer, we have to know the annual profit patterns or cash flow patterns if the cash flow pattern differs from the profit pattern of a customer. Secondly, we need to figure out how many years customers stay with your business. The figure above shows customer profits for an imaginary firm based on all factors mentioned earlier. Customer value keeps on increasing with the time for which customer stays with the company. Customer who stays 2 yrs will generate $26 of profit ($80 acquisition cost balanced in first 3 years profits $40 & $66. If the customer stays for 5 years, will generate $264 in total (-$80+$40+$66+$72+$79+$87). But the differences in customer value are very large. For the same calculations, if done for 10 years, customers will generate a net worth of $760. It would not be wise to spend $760 today for a customer who will stay with the company for 10 years as the profit generated in the future would not be equivalent to $760 today. We need to apply discount computing to take it to present value. Using a standard 15 percent discount rate will make $760 to $304. (To get the net present value of first-year profit, therefore divide $40 by 1.15, for next year divide $66 by 1.15, and so on). So for a customer who will stay with the company for 10 years, one can pay up to $304 on acquisition costs. Now we know how to calculate the value of customers based on their life expectancy of customers. Customer Acquisition Cost vs Lifetime Value Customer Acquisition Cost or CAC provides information about what losing a customer may cost your business, while Customer Lifetime Value or CLV shows how much revenue each lost customer could potentially bring to your business. With this knowledge, you can better plan your budget and strategy with your marketing team. Customer Lifetime Calculations The next question is what is the expected duration of a customer to stay with the company? To answer this, we have to find out retention rates for a customer. It is a fact that retention rates vary among customers based on age, profession, gender, acquisition source & maybe more than dozen variables. The simplest way to calculate average customer stay time is to calculate the overall defection rate and invert the fraction. First count the number of customers who defect over a period of several months, then annualize this number to get a fraction of the customer base to begin with. e.g. you lose 50 customers out of 1000 customers over three months. This works to 200 customers a year or 1/5 of all customers. Then we need to invert this number, it will become 5. So now we can say, on average, a customer stays with the company for 5 years. In percentage terms, the defection rate for customers is 20%. Lifetime Calculation Improvements To estimate customer cash flow accurately, we need to refine the above-mentioned calculations. Firstly, we have assumed defection rates are constant throughout the customer life cycles. In real life, such is never the case; defection rates are very much higher than average in the early years and much lower later on. Taking averages may lead to over or under-estimating the profit numbers. Additionally one more refinement we need to make to calculate the true value of a customer. Instead of trying to calculate the value of a single, average, static customer at a single moment, we need to think in terms of annual classes of customers at different points in their life cycles. In the real world, the company acquires new users each year. some of them defect early, others may stay for years. But the company invests money in the entire set of customers. So, to get the present value of the average customer, we must study each group separately over time. Let’s take a scenario as shown in the above image, where 100,000 new customers enter at time zero. The company invested $80 at time zero making it to a total of $80*100,000= $8 million for the whole set of customers. By end of year 1, 22% of customers defected, and only 78% left, to pay back invested 8 million. By year 5, more than half people defected. To get the present value of a customer, we will estimate the set of cash flow people generate till the time they defect. Earlier in the blog, we get the current value of the customer at $304. At a constant rate of the defection of 10%, we may be dangerously wrong in deciding the money to be invested in customer acquisition whereas the actual defection rate shown in the above image makes this number only $ 172 from $304. Imagine a company spending $200 on new customers based on earlier calculated values. It would be a completely loss-making venture. Machine Learning Scope In the above calculations, we tried to approximate the customer lifetime value & corrected ourselves initially from