AI-ML Intelligence and Learning
The emergence of Artificial Intelligence in recent years has shifted the dynamic of technology’s interaction and implementation in a way seldom seen. The fact that machines can think, analyze and operate like humans have been raising eyebrows since its inception. Artificial Intelligence, Machine Learning is deemed to be the most sought after field and career option in the coming decades. But what is so captivating about AI ML? What is it that sparked fears of AI ML taking over human labor? Inarguably, it is the essential attribute of AI – Intelligence and Learning. Intelligence Humans are the only species in the world whose intelligent quotient surpasses any other species by a wide margin. Whether due to evolution or otherworldly miracles, it is safe to say that humans stand at the top of the food chain and dominate the ecosystem like no other. This dominance can be credited to human intelligence. Although there is no definite description for intelligence, one of the greatest scientists of the 21st century, Stephen Hawking famously quoted, “Intelligence is the ability to adapt to change.” Intelligence can be thought of as an ability to acquire and apply knowledge or skills. The broad spectrum of intelligence covers abilities like understanding, logic, self-awareness, emotional experience, reasoning, planning, critical thinking and problem-solving. Although some of these abilities are found in every other animal species out there, humans surpass them by a long shot. So what is it that humans do differently and better than others. Here are a few examples: Learning Learning is an essential factor in the field of evolution. Without learning capability, humans would not make it this far. Most of the animal species have a distinct learning curve, which helped them overcome adversities and evolve accordingly. Learning is a process that causes “change” as a result of acquiring new or modifying existing knowledge, behaviors, skills, values, or preferences. Learning is very much intertwined with intelligence. Learning is an application of intelligence itself. Putting it simply, intelligence is the stirs the pot while learning is the taste of it and understanding what needs to be done. Here is how intelligence and learning interact with each other: Learning encompasses the following methodologies: Learning facilitates the prediction of data based on the understanding of the model and gathering actual observations. These observations are compared with the predicted outcomes to differentiate between the two sets. Approach to AI ML through Intelligence and Learning The conventional method of problem-solving through intelligence and learning covered a one-time pre-defined rigid model which, when running through the application, yields a definite result that couldn’t be processed any further. Such a method consists of strict one-way inputs which are mostly theoretical. This approach negates the learning process and presents a limited scope for refinement. The conventional means of that takes a lot of time and once completed, it can be considered done and dusted. The AI ML approach, however, takes a more flexible route as it enables the extraction of information from time to time, understanding the essence of information, and refine or adjust a model as per the findings. Such a method consists of various environmental inputs that might vary from time to time to draw several conclusions. The results are extracted to pull the absolute intrinsic or indispensable quality of something, which determines its character. AI ML approach to problem-solving facilitates adjustment of the model in accordance with its character to further run it through application again. Although AI ML has imitated human intelligence and learning capability to a reasonable extent, the game is far from over. Humans being complex creatures has a wide range of intelligence, namely Logical-mathematical, existential, interpersonal, linguistic, bodily-kinesthetic etc. Humans can also derive the meaning of cosmic entities far from the reach of an AI machine for now. It remains to be seen how far AI ML will go considering this is just the start.
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AI-ML Engineering Problems
Artificial Intelligence (AI), Machine Learning, and Deep Learning have been extensively used for more than a decade but largely remained confined to areas such as voice recognition, image reconstruction, image/signal processing, and output prediction. Such algorithms have seen limited usage in engineering domains such as thermal management, electronics cooling industries, fluid dynamics prediction inside the engine or over a bonnet, aerodynamics, and fluid dynamics problems across an aero-foil or turbine engine. The delicate relation between AI ML and engineering can be better explained with two specific terms – A priori knowledge and posteriori knowledge. Since the time of Emmanual Kent, western philosophy has defined A priori knowledge as something which is attained from reason and independent of particular experiences. On the contrary, posteriori knowledge is derived from real evidence that has to be considered authentic. It means A priori knowledge is not circumstance-centric but instead follows a set of pretty universal rules. Fundamental concepts of thermodynamics, electromagnetism, mechanical, and material properties are highly quantitative. They stick to a predetermined route rather than a vast stock of different scenarios. Engineering Problems Requirements Every problem related to engineering emphasizes the below-mentioned parameters: High Accuracy Levels – Every endeavor starts with a model in the early stages. The model undergoes various physical applications and virtual simulations. It is done to gather all sorts of data to determine the proposed workability of the model and improvise areas. The model goes through several stages of scrutiny until high accuracy levels are achieved. AI ML works more on input feeding, and the outputs fluctuate every given time. Function Over Feel – Engineering problems ask for the accurately intended functionality of a model. Feel of the component is never the priority. Every process applied to a model at every stage makes sure the intended functioning is obtained. As mentioned before, it is more linear. On the other hand, AI ML targets more on the feel, which varies with different situations. High Repeatability and Predictability – An engineering task involves a high repetition of activities and the desired outcome is already known. One cannot simply predict an AI ML output, and as a result, for a conventional model in engineering, AI ML is not suitable. However, recent years have witnessed increased usage of AI ML in the engineering sector, which is attributed to the following change in trends: Application of AI ML in Engineering Problems Although Artificial Intelligence has found its niche in the engineering sector, it is extensively found in four areas of operation, which have a massive importance in today’s market. Generative Design As the related data are available for every product released in the market, we have a readily available vast database to quickly conjure up past information and generate engineering data out of it. This makes the task more streamlined, so we can understand product requirements, highlight the recurrence of similar conditions in the past, and pull out past data that have previously catered to the same. This minimizes the time required to draw out an elaborate plan from scratch. If a problem is repetitive, it can be solved with the help of past data. This helps to intend to multiple issues simultaneously. Failure Analysis Failure Analysis is the collection of data and analysis to obtain the cause of a failure. Failure analysis is essential as it helps pinpoint the causes, reasons behind causes and pave a way to determine corrective actions or liabilities. A massive set of failure analysis records is fed to AI ML, which comes in handy during similar failures. AI ML can assess the loss and come back with valuable information, should the incident occurred in the past. Once again, it reduces detailed investigation and time. Digital Twins A significant aspect of AI, while digital twin has been around circa 2002, credit goes to the Internet of Things (IoT) for making it cost-effective to implement. It was named one of the top 10 technology trends for 2017, considering it is imperative to business. The digital twin is a virtual, digital replica of a real-world entity or process. The intelligent components are integrated into a physical element to gather data such as working conditions, position, and process changes. The compiled data is collected, synthesized, and integrated into a virtual model and AI algorithms. Such data assets can be created even before the physical model is built. Applying analytics into these virtual models can give back relevant insights about the real-world asset. The best part of the digital twin is that once the physical and the virtual models are integrated, the virtual model can sync with the actual model. Digital Inspection The digital inspection involves collecting information and analysis of products on production to ensure quality control. The operation of digital inspection has gained considerable momentum in engineering, especially in the manufacturing sector. Unlike paper inspections which could have been laced with occasional errors, digital inspection minimizes or completely obliterates the chances of mistakes. AI ML has made its way to production and manufacturing, consequently providing automation that is faster, cost-effective, and superior to human involvement. AI-infused digital inspections build intelligent systems that perform quality checks down to the finest of details, leaving no stones unturned. The rise of artificial intelligence has allowed automated machines to develop complicated manufacturing and design operations. AI has found significant importance in: The end goal is to introduce machines capable of learning, exploring, probing, and improving without human intervention. AI ML and Big Data are climbing the ladders of engineering with pace. An interesting point to bring up is that in our pursuit of creating supreme AIs, we are unwrapping information about how human brains perceive & operate and how we address the learning process, both consciously and unconsciously.
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Automation vs. Future Job Market: How Will It Unfold
Automation is here to stay An automated warehouse in Hong Kong that runs 24/7 uses a swarm of robots driven by AI to help deliver groceries. Known as Autonomous Mobile Robots, or AMR, they operate on a tailored track laden with QR codes to track their movements. The data they collect aids in improving their efficiency over time. The more the robots work, the smarter they become. AI has helped meet modern consumers’ demands for fast delivery. The current Covid-19 pandemic has increased markets for automated logistics. Big players in e-commerce like Amazon and Alibaba already have a horde of AI-powered robots relentlessly doing their bidding. These Robots and computerized systems running them are subsets of a much bigger field of study: Artificial Intelligence. Automation is here to stay and thrive. There is no going back from a technology that is on a mission to transform how we interact with our daily tasks. Automation is everywhere, From warehouses to factories, from mobile phones to customer support, from cab services to transportation. You name a field, and Automation is already prevailing in it. Tesla and SpaceX CEO Elon Musk claimed that AI will be smarter than humans and will overtake by 2025. Although it sounds a bit exaggerated, the rate at which AI and Automation are galloping towards the future, such predictions are entirely dismissible. However, Elon Musk also described AI as an existential threat. There have been growing concerns about AI taking over human jobs. Is it a grave threat, or is it fear-mongering? As per a leading consulting firm, one in three US employees will hand over jobs to Artificial Intelligence by 2030. How Automation is affecting various industries Automation is a derivative of great industrial revolutions that changed the production and commodity landscape. There are four industrial revolutions, the current one being the fourth industrial revolution, also known as Industry 4.0 (To read more about Industry 4.0, refer to Introduction to Industry 4.0). Coming back to Automation and its effect on industries, it is safe to say that some sectors will be receiving a more significant impact than others. Let us have a quick look at such industries ready to embrace the automation juggernaut. Manufacturing: Probably the biggest receiver of change when it comes to Automation, the manufacturing industry is a fast-evolving domain that needs rapid advancements in Automation. Intelligent machines and robots have been in use in this industry for a decade already. The need for Automation in manufacturing is to enable error-proof operation, consistent production, negligible downtime, fewer human factors, and constant pace. In a world where consumer demand is growing, one must be super-efficient to meet those demands by supplying products to the market continuously. Transportation: Transportation is one of the first industries to be affected by the automation wave. Airplanes have already been using autopilots for decades. Self-driving cars are being increasingly tested and deployed on the road. Couple that with the Internet-of-Things (IoT), and we have a robust system of intelligent vehicles. Agriculture: With the world population touching 8 billion by the end of this decade, there is a dire necessity of producing the optimum amount of food to feed the people. As a result, the agricultural sector needs increased attention regarding automating food production, distribution, and supply. Logistics: As mentioned earlier in this blog, top companies like Amazon and Alibaba have upgraded logistics at the consumer level by employing robots, placing AI technologies to manage warehouses and delivery departments. Healthcare and Pharmaceuticals: With the advent of nanotechnology, robotics, and IoT, the healthcare and pharma sector has climbed the ladder and introduced some groundbreaking medical treatments. The field of gene research and genetic altering system employs nanobots to carry out tasks. Customer Relations: Remember when you enter a website, and a pop-up generates, eager to lend you support? Or how about when you have a complaint, and you interact with a customer care executive? Well, they are most likely chatbots with curated responses to address your queries and grievances. Many retail outlets in advanced nations are adopting cashier-less automated transaction desks. Automation will end with repetitive work, and it has started shaping future jobs. It is likely that soon a lot of the current jobs will no longer exist. It is even predicted that jobs like plumbers, car mechanics, barbers, and funeral directors are likely to be replaced by automated appliances, robots, and computers. Will Automation Take Over Jobs, Or Will It Improve Them? As seen from the thriving tech sector, there is no immediate threat to jobs with AI, but a more radical use of technology could destroy employment opportunities for millions. Automation has been around since the late 1800s, but with the rise of the digital revolution, we see it gain momentum and be applied to a wide range of sectors and services. We are already witnessing Automation and the use of robots taking over repetitive and mundane processes like manufacturing and sending information to factory floors. In the transport sector, most of the workforce is being replaced by technology. The financial industry has also begun losing jobs to computers as these can perform most of the jobs that have to be done. Eventually, it may result in a world full of unemployed people and loads of robots and intelligent systems. Yes, all those possibilities could turn out to be true. There are big movie franchises that show why this is not a good idea. According to a 2013 study on the probability of automated jobs predicted that bank workers, transportation and logistics workers, and clerical and administrative workers – many middle-class jobs – were at risk of being replaced by technology. But is that fear-mongering genuine? While Automation will indeed displace many jobs over the next 10 to 15 years, it won’t eliminate human employees at all but rather modify the job landscape by introducing new work opportunities. Rather than eliminating the drudgery of repetitive tasks, Automation will place people in control of an entirely different set of operations.
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The Coronavirus shadow over outsourcing
The world has not witnessed a global recession since 2008. Experts suggest 2020 will be reminiscent of the great depression of the 1930s. The elites will lose money and the working class will lose jobs; the poor will lose lives. Such prediction and prophecies are quite rampant on the Internet and the media these days, and especially in social media. And it all revolves around the global pandemic: Coronavirus, precisely Covid-19. Let us skip past what COVID-19 is because if you are reading this, I am sure it has become a part & parcel of your daily life by now. With the world under lockdown, people wait with bated breath for the vaccine. Civil rules like social distancing, isolation, correct hygiene practices have been enforced. The public essential services are working with a limited workforce. The private companies have resorted to “work-from-home” aka WFH policy as this is the best bet that’ll keep them going. Coronavirus has exposed the vulnerability of the supply chain and pushed the IT industry in a complex rut. Most IT companies need IT service providers and the pandemic just “threw a monkey wrench in the wheel”. Almost every IT companies have mandated that their employees work from home. For now, video conferencing, cloud storage and collaboration tools are managing tasks seamlessly. Companies are leaving no stones unturned in making sure that their workers have the necessary tools and technologies to ensure speed, security, quality of services provided. In offshore locations, where much of the workforce is not familiar with the work-from-home scenario, is facing tactical and operational hassles such as getting laptops, setting up VPN, VDI, or Citrix access; seamless WiFi connectivity and ensuring product security. The IT industry has faced one of the worst crises ever in the face of coronavirus pandemic. Companies have shut down workplaces, stores, and processing plants. With the looming possibility of lockdown extensions, as the coronavirus spreads, many big companies have already charted out elaborate WFH plans. For example, TCS has already mandated that post- COVID-19, 75% of 4.5 lakh employees will permanently work from home. Although this is just a solution to keep the wheels rolling, the coronavirus pandemic has unleashed greater impacts incorporate business machine then what meets the eye. With economies closed, borders sealed, outsourcing has become a daunting task all of a sudden. Here is how the Covid-19 has affected IT industry and outsourcing: Continuation of business Let us be honest, most outsourcing business continuity plans were not designed to face a global pandemic and neither was ready to apply widespread work-from-home operations. In such trying time clients need to quickly assess the state of business continuity and take steps to address any holes or hassles. As of April, India was operating at around 80% of its pre-pandemic productivity with WFH fully enforced among 9 out of 10 employees. However, there are some companies repatriating services while some have opted for alternate delivery methods. It is expected that business continuation during a global disaster should be molded in a way that addresses such times. Productivity and Performance Coronavirus is already making impacts on productivity, either on a group or an individual. The sales department has witnessed it, particularly as their bonus targets are getting hampered due to uncertain situations. WFH is a sudden realization for every IT employee and churning daily productivity while maintaining performance is not consistent anymore. Another issue is with small and medium enterprises (SMEs) in India. They are the worst hit and cannot provide provisions for WFH for their employees Hence, either they have cut down their production and some have resorted to lay-offs. Effect on the Global supply chain Coronavirus pandemic has had a deep impact on logistics and supply chain. As of now, the cost of supplies from China has increased owing to expedited freight costs and paying premiums. Many industries in India are working out an alternative sourcing option, although chalking out and identifying an alternative supply system is not very easy. It is safe to say that, the more this pandemic extends, the more recovery time will be taken by the supply chain, which will affect all major countries. Closure of workplaces and facilities As the coronavirus kept on spreading at a rapid rate across the world, the first thing most governments did was enforcing lockdown. Absolute isolation. Of course, it is done with the noble intention of safeguarding citizens. Some governments have imposed long lockdown periods to let people familiarize themselves with it. However, this presents a problem for so many businesses as lockdown resulted in less footfall. Many of our clients from the States have reported a substantial drop in business due to closure: “It is great that our government is considering lockdown and stimulus package for corporations. However, when all our businesses collapse, who will provide these individuals with jobs? It is difficult enough to carry on any business in the state of California because of government oversight.” Said one of our clients with a worried voice. Work-From-Home hassles Although many companies have mandated WFH as they had full provisions and access to necessary tools, there a lot many others who are having a hard time providing their employees remote working facilities, mostly due to the lack of collaboration tools and interpersonal training. A report by a leading IT service management company suggests that 51% of the private companies in India do not have enough technology and resources to implement a full work-from-home strategy. While the non-IT firms struggle with decades-old systems, storage backup, and poor connectivity, the small and medium scale IT firms do not have adequate access to conferencing tools like Zoom or Skype or VPN and/or Citrix. Will, the corporate world resort to a new outsourcing model once this is over? If this is over? In case the pandemic stays for quite some time it is evident that the outsourcing landscape will witness a massive reshuffle. There might be an entirely new economic and world business model altogether. As of
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Why and when should a company outsource software development
There are two proven aspects tech organizations rely on to survive and outpace their competitors in the market:Control, under which cost, resources, and capabilities are managed and Speed, which makes sure the product appears at the market. For these two ensure a steady flow of benefits and future bloom.But it is not a case of black and white but rather, multiple shades of grey as making a product is not the only task you have to tend to. The aspects of management, marketing, sales, analysis, employee management come into play. Being a jack of all trades is appreciable but not practical when you are running your own company. You need a helping hand and this is where outsourcing software development comes in. Software development outsourcing is a process followed by companies that hire a third-party software contractor to do the particular software related task that could have been done in-house. Developing a complete software application in-house asks for both money as well as time and to be honest, not everyone has an extended IT team. In these circumstances, tech companies turn to Software outsourcing companies.For the record, stats show that annual global revenue from the information technology field by outsourcing software development has been estimated to be around 60 billion $ as of 2019.Outsourcing software development projects help businesses achieve a higher economy of scale and let them focus on their core competency without spending a considerable amount of time and money.While you are considering outsourcing software development to a 3rd party, you are probably questioning about quality of work, the budget required, and the overall risk associated with outsourcing. This write-up intends to focus on the benefits of outsourcing software work. They are as follows: Access to a large pool of skilled resources Generally, outsourced companies have access to a larger talent pool of experienced and skilled specialists in the industry, owing to their years of collaboration with multiple firms from varied fields. Outsourcing helps you to find the right talents for the right project while shifting off your human-resource issues. Many companies also have an interviewing facility with an expert panel where you get an opportunity to select the expert candidates with the right skills and experience fit for your project. Most of the software application development companies maintain an extensive database of skilled employees. Adore flexibility Outsourcing software development allows for flexibility within business operations. Human resources are one such area which gets a breather, as you do not need to be attentive to recruiting and hiring procedures. Plus, you are worry-free about scaling your employees to change team size from one project to another project. Save on time & cost Outsourcing saves an immense amount of time spent on recruiting, training, and settling employees for in-house projects. And let us not kid ourselves, the biggest reason for people to outsource is to cut costs. Outsourced projects reduce development expenses by 60% – 70% less than in-house projects and help to reduce your workload. One of the important cost-saving factors is that you don’t have to perform upfront investment. Time-saving and cost reduction has always been the first choice of any business. And, outsourcing makes sure these issues are well addressed and taken care of. Perks of advanced technologies It is one of the prime reasons to hire a third party to develop a software application. Keeping up with modern technology space is a daunting task as it evolves day to day and keeps on introducing techs. By authorizing your software project requirements to a software application development company, you enable them to implement the latest technology for your project. These new trends and technologies are a result of such companies having years and years of experience with clients from various fields. Outsourcing has brought a new perspective to the workspace which encourages new technology put up by specialists who possess diverse technological expertise. Therefore, one need not worry about overhauls of ever-changing technology. Risk alleviation Outsourcing your software development task means you do not have to worry about risks associated with the development cost, resource allocation, on the failure of the development. As you hand over the responsibility to the 3rd party vendor, it becomes their prime responsibility to deliver the pre-defined outcomes for you. So outsourcing pretty much alleviates the risk. High-quality services While you go for outsourcing services, the outside firm guarantees high-quality services with zero downtime. And, they deliver quality results all the time. Outsourcing also opens up opportunities for up to-the-mark custom software solutions. The biggest benefits of outsourcing are high-grade services. This is the reason why more and more companies are selecting for outsourcing. Post-deployment services The team which worked on your software development is also capable of solving all the issues related to it. Hence, when faced with product complexities, the outsourced team will help you to fix it. A lot of outsourced companies have a 24×7 support team to assist you with issues about the concerned project. This ensures a hassle-free experience with great services. The service provider companies offer support, commitment, and maintenance throughout the project and also after completion of the project. Transparent work policy This case particularly helps when you have outsourced to a reputed company. When you are outsourcing, you might be clouded with concerns about privacy policy. Sharing an idea with an outside party is always a matter to ponder upon. But when working with a reputed organization, they will make sure full work transparency is maintained and all the compliance standards are adhered to. The catch is, you need to know which service provider to approach and the nature of engagement revolving around agreement papers and legal hassles. To obtain digital transformation and growth through innovation while reducing risks and increasing profit simultaneously, outsourcing software development is a great tool. It also helps businesses achieve competitive advantage without losing focus and efficiency. Your product remains in capable, trustworthy, professional hands, and you get to focus on running your business. It is a
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How do I outsource software development
Outsourcing software design and development has become a global trend in today’s fast-paced market. The market size of IT outsourcing has been estimated to be worth $66.52 billion in 2019. 59% of companies have resorted to outsourcing software development as a means of cutting expenses and approximately half of the companies based in the United States have resorted to software outsourcing practice at least once, for some reason. In the dynamic technology market, the local talent pool often seems to hit the roof as the race for churning the best of technology keeps pacing up. Thus, companies often have to reach out and look for global talent across the world. As a software development project takes a considerable amount of time and money, an organization often falls short of required time, manpower & money. This is where assistance from an experienced professional or a dedicated outsourcing firm comes into play. Assuming that your company has decided to finally outsource software development and considering how there are a plethora of software developers these days, finding a top-notch software development outsourcing company that meets your project requirements becomes increasingly more difficult. But be attentive, as the quality of your product depends upon the vendor you choose to work with. Normally, you would want the outsourcing partner to leverage their experience and set up a platform for the smooth integration of services. However, you being the owner/caretaker, must leave no stones unturned to ensure you have outsourced your work to the right party. Let us go through a step-by-step process that elaborates how to outsource software development projects carefully and effectively: Define Objectives and final goal A famous quote says “a person without an aim is like a ship without a rudder”. Here, in this case, the person is your product and the aim is the outcome the way you wanted it to be. Your product will not take off or crashland in the middle if you do not set your goal from the get-go and define a pathway to achieve that. This is a highly important stage where proper communication is required within your own company first and then with the external partner to present a clear blueprint of how this is going to be. Look out for the best firms for outsourcing and make a list To make sure you work with the best, sort out and make a list of possible outsourcing partners whom you see fit the role on a glance. Don’t just stop with the local vendors but if possible, reach out and research specific countries that have a good outsourcing culture. Some good choices these days include Ukraine, Hungary, South Korea, China, and India. Note down the particulars regarding how much do the software developers charge in those countries per hour, the time difference between your location and theirs, the legal procedures in stipulated for making contract and the location of agency or developer to ensure smooth communication. Research the best local software development outsourcing companies Once you have a list of five to six agencies, start researching each company. Here are some of the factors you need to focus on while researching: Contact Offshore software development agencies about your project Now in the next step, reach out to the companies you have previously reviewed and shortlisted. At this stage, it is essential to communicate your product goals and requirements with the utmost clarity to ensure that you and the outsource software development agency are on the same wavelength. Take this time to share ideas and ask as many questions as possible. Select the best software development firm to outsource When an agency doesn’t respond to your queries for a week or longer, it is a sign of an unreliable partner. What you need is a prompt response from someone, especially when there time difference between you. One more fact, a “yes” and “yes we can” answer to every question should turn on the alert button in your mind. Nothing is perfect and no one knows everything. That is a sign of over-confidence, not truthfulness. After all, trust is the key.Once you have finalized your choice, based on research and interviews you’ve conducted, put your agreement to paper (or an electronic one). Here are a few documents you would want the outsourced to sign before taking off with project: To outsource or not to outsource software development? only you can answer that question after a careful assessment of your company’s needs, manpower, budget, and a few other things. The choice is not that easy to make and you have to consider a lot of factors before you do.
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Issues in CAD Software – User’s perspective
5 Factors to consider while choosing a CAD platform Choosing a CAD platform can be a very difficult decision for any organization. Depending on the size of the organization it could be a very crucial decision because it is “sticky decision” and can not be changed easily in the future. So the decision should be taken considering a variety of factors. This document discusses some of these factors in detail. In a recent survey conducted by an eminent website, 230 product development professionals were asked questions regarding their level of satisfaction with CAD software. The survey intended to gather individual experiences of CAD users, turn them into informative insights and churn out common issues faced by design teams et al. The common issues were grouped under specific causes and in a total, four major issues were drawn out. Suitability of CAD software The most important aspect perhaps is the suitability of the CAD system for a particular organization. It is always a good idea to list out all the workflows, representative parts, any special processes, etc. Then a benchmark study should be conducted to assess the suitability of different CAD software against the checklist. One can even rate different software on each of the parameters. Software Ecosystem This is an external factor but an important one. Suppose you need to work with a lot of vendors then one has to consider that aspect. Would my vendor be able to provide me data in my format? On the other hand, your customers may force you to provide data in a specific CAD format.The availability of trained resources is also an important consideration to ensure that you can attract and retain talent for your business needs. The learning curve for CAD software When a new CAD package is introduced, the amount of time taken for the users to learn the new features is critical to how precisely and quickly design teams can bring their ideas to life.Although companies provide specific, coherent, and comprehensive training regarding new CAD software, it’s not enough of course, as the user also has to familiarize themselves with the interface of the new CAD package. Needless to say, the time consumed in this regard has caused quite a bit of inconvenience. Interoperability Importing and exporting files correctly shouldn’t be a hassle in general. However, this issue stood in the second place as CAD users found importing/exporting or interoperability quite the headache. The primary problem that CAD users face while importing and exporting files, is that the 3D model loses features—if it has no parameters, it has no intelligence—and therefore it is no longer parametric. Sometimes the object is incomplete or just a partial translation, which means the surfaces are missing. The quick emergence of varied CAD software has led designers to democratize, leading to the usage of multiple CAD systems in the design process, thus challenging the CAD interoperability aggressively. Different suppliers require different CAD platforms. It depends on many factors, primarily the nature of the task and product upon which it has to work. Merging different CAD data together without affecting the design intent is quite the hassle. Although, a lot of software these days support different CAD files, there are instances, where the particulars of a project has made the product confined to that one CAD software. Interoperability eases up extra work and whether to make your own software compatible with other, is a decision that should be seriously taken into account. Cost of Ownership The cost of ownership is a big deal among users. Whether it is about the actual cost that concerns users or the fact that they don’t perceive sufficient value, the cost of ownership has always remained a matter to frown for users. A possible cause might be unawareness in the case of the user about an important new functionality available in modern CAD systems that can massively enhance product development processes. Some of these features enable better ways of creating and managing documentation as well as useful tools such as generative design and simulation.To better understand how leveraging new functionalities can offset the notion that CAD software costs are too high, we can weigh on one of the new features, simulation, which compliments the model design. The companies identifying design issues early on in the design cycle are actively using simulation in the said phase thereby integrating it into their design. Simulation aids in iterating the design and making varied choices much earlier in the phase rather than making that choice much later during the prototype phase. In the bottom line, product development professionals want an affordable CAD system, and that provides value to their designs. Interoperability remains a major hindrance that seems quite unnecessary and outdated in this era. Design professionals want their CAD system to be familiar in interaction and easier to use and want that usability to translate into an easier search and hiring process.
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Brief history of Artificial Intelligence (AI)
In November 2014, E-commerce giant Amazon announced the launch of Alexa, a voice-controlled virtual assistant whose task is to transform words into action. It caught the attention of tech enthusiasts and the general populace alike. The inclusion of Samuel L. Jackson’s voice in Alexa was the talk of the tech town. Recent years have witnessed a climactic change in the way technology interacts with humans. Alexa happens to be just that one card out of the deck. From Tesla’s cybertruck to internet giant Facebook’s Edge Rank and Google’s PageRank has called for both awe and a little bit of commotion within the tech community. The driving force behind such innovations can be put under a single umbrella term — Artificial Intelligence or AI. Artificial intelligence (AI) can be defined as — the simulation of human intelligence in machines, especially computer systems and robotics. The machines are programmed to think and mimic human actions such as learning, identifying, and problem-solving. Although AI has burst into the scene nowadays, the history of AI goes way before the term was first coined. It is safe to say that the principle is derived from the Automata theory and found references in many storybooks and novels. Early ideas about thinking machines emerged in the late 1940s to ’50s by the likes of Alan Turing or Von Neumann. Alan Turing famously created the imitation game,now called the Turing Test. After initial enthusiasm and funding on machine intelligence until the early 1960s,entered a decade of silence. It was the period of reduced interest and funding on research and development of AI. This period of decline is known as ‘AI Winter.’ Commercial ventures and financial assistance dried up and AI was put on hibernation for the said period. The late 1970s witnessed a renewed interest in AI. American machine learning pioneer Paul Werbos devised the process of training artificial neural networks through backpropagation of errors. In simple terms — Back Propagation is a learning algorithm for training multi-layer perceptrons, also known as Artificial Neural Networks. The neural networks consist of a set of algorithms that loosely mimics a human brain. It means much like a human brain; it is designed to interpret sensory data, cluster raw inputs, and classify them accordingly. 1986 saw the backpropagation gaining widespread recognition through the efforts of David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams. In 1993, Wan became the first person to win the international pattern recognition contest with the help of the backpropagation process. Since the emergence of computers and artificial intelligence, computer scientists have drawn parallels between these intelligent machines and human minds. The comparison reached a pinnacle when, in 1997, an information technology company, IBM, created a computer known as Deep Blue to participate in a chess match with renowned chess master Gary Kasparov. The match went on for several days and received massive media coverage. After a six-game match, Gary Kasparov secured a win, Deep Blue secured two wins and rest three draws. The highlight of the spectacle, however, was the ability of machines to push forward the boundaries and lay down a new benchmark for computers. Deep Blue made an impact on computing in many different industries. It enabled computer scientists to explore and develop ways to design a computer to tackle complex human problems with the help of deep knowledge to analyze a higher number of possible outcomes. The rise in popularity of social media with Facebook saw the implementation of AI/ML in a wide array of applications. One prominent characteristic was the use of DeepFace. As the name suggests, DeepFace is a deep learning facial recognition system designed to identify human faces in digital images. DeepFace was trained on four million images uploaded by Facebook users and is said to reach an accuracy of 97%. Not so long after, NVIDIA launched Generative Adversarial Network (GAN), which is a class of machine learning designed to generate new data with the same inputs provided. The portraits created by GAN is so realistic that a human eye can be fooled into thinking it as a real snapshot of a person. GAN has seen widespread usage in the creation of celebrity faces. Google’s popular doodles are an outcome of the GAN system. The advent and rise of AI, however, has generated quite of bit of negative speculations as well, owing to recent developments in the said field. Some key concerns are as follows: While there are certainly lots of speculations for AI, we expect that the next AI winter would not come. Another AI winter is possible if we repeat the past circumstances. As for now, AI is becoming a part of our daily lives. It is in our cars, phones, and other technologies we use on a day-to-day basis. It is common to interact with AI regularly, whether it is a helping chatbot, personalized ad or better movie show/TV suggestions. AI is too much integrated into our lives and only time will tell where it heads.
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Insourcing-vs-outsourcing
Both insourcing and outsourcing are feasible ways of bringing in labor or specialty skills for a business without hiring permanent employees. When it comes to selecting between outsourcing and insourcing, several entrepreneurs cannot decide what is best for them. Before jumping on to the differences between these two business practices, we need to check the definition of the terms. Insourcing is the practice of assigning a task or function to an individual or group inside a company. The work that would have been contracted out is performed in house. Outsourcing is the act of assigning a task or function to a third party vendor instead of having it performed in-house. Differences between Insourcing and Outsourcing Insourcing is more preferrable when the business requirement is for a limited time or temporary or involves little investment. Outsourcing weighs more when businesses need to cut costs while still in need of expert professionals.
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Insourcing – A Breakdown
Outsourcing has remained an integral aspect of striking deals between engineering and design firms. While it has been growing at a solid pace each year, several companies have taken the route to insource a part of their formerly outsourced services portfolio. Insourcing is the practice of assigning a task to an individual or group inside a company. The work that would have been contracted out is performed in house. Insourcing is entirely opposed to outsourcing where the work is contracted outside. Insourcing encircles any work assigned to an individual, team, department or other groups within an organization. It is a task or function that a firm could also outsource to a vendor, being directed in-roads. It often involves getting specialists with relevant expertise to fill temporary needs or train existing professionals to execute tasks without the need to outsource the same. The group of professionals could either be direct employees of the organization or hired expertise from outside third party vendors. A perfect example can be put in this way – a company based in India opens a plant in the United States and employs American workers to work on Indian products. From the Indian perspective, this is outsourcing, but from the American perspective, it is insourcing. Causes of Insourcing The leading reasons for insourcing include: Reasons to Insource While executing an insourcing project can be achieved, it is essential to know that insourcing a service can be more complicated than outsourcing the same. The transition may require rebuilding services and leveraging capabilities from ground level that were once wholly owned by the service provider, which can turn out to be more complicated than expected.
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