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Proof That MACHINE LEARNING JOB OPPORTUNITIES 2022

• Posted by Jack Maxwell September 6, 2022 Filed in Technology #Online Machine Learning course  #Machine Learning course in Noida  #Online Machine Learning Training  #Machine Learning Training in Noida  10 views
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  Machine Learning or Automation: What’s the Difference?

  

  The idea of automation has been around for a long time; it refers to any system that performs standardized, repetitive actions.

  Machine Learning is a rapidly developing subset of artificial intelligence that focuses on analyzing large data quantities, typically to make predictions. 

  AI systems can’t truly think yet, but many Machine Learning systems exhibit AI-type behaviors, such as language recognition and visual memory comparisons.

  This article is for business leaders and professionals interested in how automation, Machine Learning and AI may impact business. 

  Major players in the tech industry are pushing the boundaries of self-determining computers, especially as cutting-edge technologies like artificial intelligence (AI) and Machine Learning become more mainstream. While many professionals understand that these technologies will make their jobs easier – or even take over specific tasks – others are feeling confusion. One common question is, what’s the difference between Machine Learning and automation?

  What’s the difference between Machine Learning and automation?

  Let’s start with Machine Learning, a subset of AI.

  “It’s an evolution,” said Andreas Roell, managing partner of Analytics Ventures, a consultancy that helps businesses adopt AI. “AI fits into the bucket of workload analysis or task analysis. Business intelligence also sits in that same bucket. It’s taking data, then analyzing it.”

  Machine Learning is typically a later-stage development, where machines take in data on their own and then analyze it, Roell explained. The biggest difference is that “Machine Learning identifies data signals relevant for the future,” he added. 

  Automation is frequently confused with AI. Like automation, AI is designed to streamline tasks and speed workflows. But the difference is that automation is fixed solely on repetitive, instructive tasks, and after it performs a job, it thinks no further.

  There’s a good chance you use automation without even realizing it – for example, by automating emails to customers, automating the way you generate invoices, or automatically logging a help-desk inquiry. Workplace automation saves time and allows workers to focus on higher-priority initiatives. It’s a reliable, computerized workhorse, able to show up and get the job done. 

  Machine Learning takes these tasks and layers them in an element of prediction. Whereas automation would continue to do exactly as you requested – say, send invoices on a specific day – Machine Learning predicts when the invoices should go out, who did or did not receive one, and when payments are on the verge of being late.

  Is AI the same as automation?

  No, AI and automation are not the same. Automation involves an entire category of technologies that provide activity or work without human involvement. For example, say an old-style water wheel represents automation, translating the power of falling water into a repetitive nonhuman activity or mechanical work. There is nothing about the water wheel that involves artificial intelligence; it just keeps doing the same thing over and over.

  We often associate automation with computers, but it’s been around for ages.

  “If you can take the resources that you have and come up with some sort of silver bullet and that turns them into radically better efficiency for what you’re getting back, that is going to be evolutionary dynamite,” zoologist Antone Martinho-Truswell told Gizmodo. “You’re going to do fantastically well, as we have. Our nearest relatives are all endangered because of us.”

  AI, on the other hand, involves a machine exhibiting and practicing something similar to what we describe as human thinking – that is, the ability to interact in thousands of ways with the world around us without receiving any prior explicit coding or instructions. Think, for instance, of how AI digital assistants like Siri or Alexa can understand and respond to our questions and commands. 

  The rate at which companies adopt AI is continuing to grow. Companies that have adopted AI are finding extensive cost savings, according to the McKinsey report on the state of AI in 2021, which interviewed 1,800 business leaders from various industries worldwide. 

  Did you know?: According to the McKinsey survey results, 27% of companies that responded found an increase attributable to AI of at least 5% in earnings before interest and taxes (EBIT).

  Will AI create new job opportunities?

  Machine Learning works to understand data, leveraging what Roell called data signals to drive future intelligence. It’s not simply performing an “if X, then Y” task stream; it’s essentially thinking through data much like a human.

  “There’s a lot of fear around AI, that it will eliminate jobs,” Roell said. “That’s not what it’s supposed to do; it’s making the way we work easier. But what it will do is lead to entirely new categories of jobs being created.”

  Roell gave the example of call center employees now being used to categorize the vast amounts of data used by AI. Several companies have taken this approach.

  “Now that is true innovation,” Roell said.

  Can Machine Learning be automated?

  Machine Learning can be automated when it involves the same activity again and again. However, the fundamental nature of Machine Learning deals with the opposite: variable conditions. In this regard, Machine Learning must be able to function independently and with different solutions to match different demands. There is a higher likelihood that Machine Learning would be applied to determining unknown prediction scenarios.

  However, the principle could apply in automated systems as a safeguard or as an element of automation, according to the Brookings Institution. For example, a computer system used to move Amazon.com boxes could learn millions upon millions of weights so that it could flag a box on the conveyor belt that doesn’t match known inventory when it senses the anomaly along the way from the shelf to the shipping truck.

  Did you know?: Workplace automation software uses rule-based logic to automate manual work, such as sending follow-up emails.

  Will we see an AI world in 2022?

  Not quite, according to Stanford University’s Artificial Intelligence Index Report 2022. AI systems are a business tech trend being more broadly deployed into the global economy. With this increasing deployment comes a commensurate increase in AI capabilities. Language models are becoming ever more accurate, image classification training times are quickly decreasing, and AI systems are increasingly affordable. 

  However, there are still substantial gaps between AI systems focused on particular activities and general-purpose thinking AI systems. 

  AI and Advance Machine Learning in BFSI Market – Overview

  The global AI and advance Machine Learning in BFSI market is projected to expand at a favorable rate from 2021 to 2031 (forecast period). The growing use of data collection technologies by banks and financial institutions is expected to have a beneficial influence on the growth of the BFSI sector. Moreover, increased investment in AI and Machine Learning by BFSI organizations and demand for personalized financial services are projected to drive global AI and advance Machine Learning in BFSI market. Furthermore, a spike in the use of modern applications in the BFSI sector is likely to provide lucrative prospects for market development throughout the forecast period.

  AI and Advance Machine Learning in BFSI Market – Competitive Landscape

  Leading players in the AI and advance Machine Learning in BFSI market are Amazon Web Services Inc., Fair Isaac Corporation, BigML, Inc, Microsoft Corporation, Cisco Systems, Inc., Hewlett Packard Enterprise Development LP, RapidMiner, Inc., International Business Machines Corporation, SAP SE and SAS Institute Inc.

  These companies are using a variety of techniques to enhance their market penetration and boost their position in the competitive AI and advanced Machine Learning in the BFSI market.

  AI and Advance Machine Learning in BFSI Market – Trends and Opportunities

  BFSI firms are boosting their investments in Machine Learning and AI technologies in order to alter the fintech management process and give enhanced services to end consumers. Moreover, with the rising BFSI sector’s intricacy and rivalry, the demand for industry-particular solutions to accomplish its objectives is growing. As a result, in order to match client demands, many banking institutions and fintech firms are investing in AI solutions, which are expected to support the global AI and advance Machine Learning in BFSI market.

  Furthermore, Machine Learning and AI can help financial institutions in many phases of the risk management process, such as detecting risk exposure, evaluating, measuring, and analyzing its consequences. Additionally, BFSI firms are embracing and creating Machine Learning methods to analyze enormous amounts of data and provide relevant insights to clients. In addition, increased investments in AI and advanced Machine Learning by banks and fintech to improve automation and provide a more efficient and customized client experience are projected to boost the global AI and advance Machine Learning in BFSI market.

  Major financial institutions such as Bank of America, Morgan Stanley, and JPMorgan are investing significantly in Machine Learning technology to create automated financial advisers and upskill systems to identify issues like money laundering techniques, which can be avoided through financial monitoring. Such factors are expected to create significant revenue-generation opportunities in global AI and advance Machine Learning in BFSI market.

  Moreover, due to the rising use of chatbots across banks and increased rivalry among BFSI enterprises for a greater share of the market, end consumers are constantly requesting customized financial services. Various BFSI firms are offering budget management applications driven by Machine Learning, which assist clients in meeting their financial goals and improving their money management process, hence boosting market development.

  During the COVID-19 pandemic, the global AI and advance Machine Learning in BFSI market have shown consistent development. With the advent of the COVID-19 pandemic, insurers and organizations boosted their use of AI to handle remote workforces, develop their digital services to assist distribution, and enhance their online platforms. Moreover, with fast digital transformation, several governments have adopted strict requirements to safeguard end users’ data, such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR).

  AI and Advance Machine Learning in BFSI Market – Regional Landscape

  The AI and advance Machine Learning in BFSI market in North America is expected to grow during the forecast period. This is due to a variety of variables, including the entry of new sectors and economic growth. Moreover, Canada and the United States are likely to be the largest revenue generator in AI and advance Machine Learning in BFSI market in North America.

  The Asia Pacific AI and advance Machine Learning in BFSI market is projected to expand during the forecast period. This is due to the widespread presence of small and medium-sized enterprises, which are increasingly turning to hosted AI and advanced Machine Learning in BFSI solutions to effectively handle their business operations, especially in emerging economies such as India, China, and Singapore.

  • What’s the Difference Between Vertical Farming and Machine Learning?
  • What you’ll learn
  • What is vertical farming?
  • Why is this author comparing it to Machine Learning?

  Is there really any relationship between these two things?

  Sometimes inspiration comes in the oddest ways. I like to watch CBS News Sunday Morning because of the variety of stories they air. Recently, they did one on “Vertical Farming - A New Form of Agriculture”.

  CBS News Sunday Morning recently did a piece on vertical farming that spawned this article.

  For those who didn’t watch the video, vertical farming is essentially a method of indoor farming using hydroponics. Hydroponics isn’t new; it’s a subset of hydroculture where crops are grown without soil. Instead, the plants grow in a mineral-enriched water. This can be done in conjunction with sunlight but typically an artificial light source is used.

  The approach is useful in areas that don’t provide enough light, or at times or in locations where the temperature or conditions outside would not be conducive for growing plants.

  Vertical farming is hydroponics taken to the extreme, with stacks upon stacks of trays with plants under an array of lights. These days, the lights typically are LEDs because of their efficiency and the ability to generate the type of light most useful for plant growth. Automation can be used to streamline planting, support, and harvesting.

  A building can house a vertical farm anywhere in the world, including in the middle of a city. Though lots of water is required, it’s recycled, making it more efficient than other forms of agriculture.

  Like many technologies, the opportunities are great if you ignore the details. That’s where my usual contrary nature came into play, though, since I followed up my initial interest by looking for limitations or problems related to vertical farming. Of course, I found quite a few and then noticed that many of the general issues applied to another topic I cover a lot—Machine Learning/artificial intelligence (ML/AI).

  If you made it this far, you know how I’m looking at the difference between Machine Learning and vertical farming. They obviously have no relationship in terms of their technology and implementation, but they do have much in common when one looks at the potential problems and solutions related to those technologies.

  As electronic system designers and developers, we constantly deal with potential solutions and their tradeoffs. Machine Learning is one of those generic categories that has proven useful in many instances. However, one must be wary of the issues underlying those flashy approaches.

  Experts Wanted

  Vertical farming, like Machine Learning, is something one can dabble in. To be successful, though, it helps to have an expert or at least someone who can quickly gain that experience. This tends to be the case with new and renewed technologies in general. I suspect significantly more ML experts are available these days for a number of reasons like the cost of hardware, but the demand remains high.

  Vertical farming uses a good bit of computer automation. The choice of plants, fertilizers, and other aspects of hydropic farming are critical to the success of the farm. Then there’s the maintenance aspect. ML-based solutions are one way of reducing the expertise or time required by the staff to support the system.

  ML programmers and developers also are able to obtain easier-to-use tools, thereby reducing the amount of expertise and training required to take advantage of ML solutions. These tools often incorporate their own ML models, which are different than those being generated.

  Profitable for Only a Few Types of Plants

  Hydroponics works well for many plants, but unfortunately for multiple others, that’s not the case. For example, crops like microgreens work well. However, a cherry or apple tree often struggles with this treatment.

  ML suffers from the same problem in that it’s not applicable to all computational chores. But, unlike vertical farms, ML applications and solutions are more diverse. The challenge for developers comes down to understanding where ML is and isn’t applicable. Trying to force-fit a machine-learning model to handle a particular problem can result in a solution that provides poor results at high cost.

  High Power Consumption

  Vertical farms require power for lighting and to move liquid. ML applications tend to do lots of computation and thus require a good deal of power compared to other computational requirements. One big difference between the two is that ML solutions are scalable and hardware tradeoffs can be significant.

  For example, ML hardware can improve performance that’s orders of magnitude better than software solutions while reducing power requirements. Likewise, even software-only solutions may be efficient enough to do useful work even while using little power, simply because developers have made the ML models work within the limitations of their design. Vertical farms do not have this flexibility.

  High Investment and Running Costs

  Large vertical farms do require a major investment, and they’re not cheap to run due to their scale. The same is true for cloud-based ML solutions utilizing the latest in disaggregated cloud-computing centers. Such data centers are leveraging technologies like SmartNIC and smart storage to use ML models closer to communication and storage than was possible in the past.

  The big difference with vertical farming versus ML is scalability. It’s now practical for multiple ML models to be running in a smartwatch with a dozen sensors. But that doesn’t compare to dealing with agriculture that must scale with the rest of the physical world requirements, such as the plants themselves.

  Still, these days, ML does require a significant investment with respect to development and developing the experience to adequately apply ML. Software and hardware vendors have been working to lower both the startup and long-term development costs, which has been further augmented by the plethora of free software tools and low-cost hardware that’s now generally available.

  Technology Failure Can Lead to Major Problems

  Cut the power on a vertical farm and things come to a grinding halt rather quickly, although it’s not like having an airplane lose power at 10,000 feet. Still, plants do need sustenance and light, though they’re accustomed to changes over time. Nonetheless, responding to failures within the system is important to the system’s long-term usefulness.

  ML applications tend to require electricity to run, but that tends to be true of the entire system. A more subtle problem with ML applications is the source of input, which is typically sensors such as cameras, temperature sensors, etc. Determining whether the input data is accurate can be challenging; in many cases, designers simply assume that this information is accurate. Applications such as self-driving cars often use redundant and alternative inputs to provide a more robust set of inputs.

  Changing Technology

  Vertical-farming technology continues to change and become more refined, but it’s still maturing. The same is true for Machine Learning, though the comparison is like something between a penny bank and Fort Knox. There are simply more ML solutions, many of which are very mature with millions of practical applications.

  That said, ML technologies and applications are so varied, and the rate of change so large, that keeping up with what’s available—let alone how things work in detail—can be overwhelming.

  Vertical farming is benefiting from advances in technology from robotics to sensors to ML. The ability to track plant growth, germination, and detecting pests are just a few tasks that apply across all of agriculture, including vertical farming.

  Wrapping Up

  As with many “What’s the Difference” articles, the comparisons are not necessarily one-to-one, but hopefully you picked up something about ML or vertical farms that was of interest. Many issues don’t map well, like problems of pollination for vertical farms. Though the output of vertical farms will likely feed some ML developers, ML is likely to play a more important part in vertical farming given the level of automation possible with sensors, robots, and ML monitoring now available.

  Top 25 Machine Learning Interview Questions

   

  • What are the basic differences between Machine Learning and Deep Learning?

  • What is the difference between Bias and Variance?

  • What is the difference between supervised and unsupervised Machine Learning?

  • What are the three stages of model building in Machine Learning?

  • What are the applications of supervised Machine Learning?

  • What are the techniques of Unsupervised Machine Learning?

  • What are the different types of Machine Learning?

  • What is Deep Learning?

  • Comparison between Machine Learning and Big Data

  • Explain what is precision and Recall?

  • What is your favorite algorithm and also explain the algorithm briefly in a minute?

  • What is the difference between Type1 and Type2 errors?

  • Define what is Fourier Transform in a single sentence?

  • What is deep learning?

  • What is the F1 score?

  • How is the F1 score is used?

  • How can you ensure that you are not overfitting with a particular model?

  • How to handle or missing data in a dataset?

  • Do you have any relevant experience on Spark or any of the big data tools that are used for Machine Learning?

  • Pick an algorithm and write a Pseudocode for the same?

  • What is the difference between an array and a Linked list?

  • Define a hash table?

  • Mention any one of the data visualization tools that you are familiar with?

  • What is your opinion on our current data process?

  • Please let us know what was your last read book or learning paper on Machine Learning?

   

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  A passion for working with cutting-edge technologies and these staggering stats alone are reasons enough to pursue a career in Machine Learning. Professionals all over the world are turning towards credible Online Machine Learning training or Machine Learning Training in Noida that impart practical knowledge. And, this ML course from Talentedge boasts of quality content and practical knowledge, all of which is enclosed in a structured learning approach wherein lectures are delivered LIVE via eminent faculty of top-rated institutions.