Collaborative filtering (CF) based on the alternating least squares (ALS) technique is another algorithm used to generate recommendations. It produces automatic predictions (filtering) about the interests of a user by collecting preferences from many other users (collaborating). The underlying assumption of the CF approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than a randomly chosen person. This algorithm gained a lot of traction in the data science community after it was used by the team winner of the Netflix Prize. The CF algorithm has also been implemented in Spark MLlib with the aim of addressing fast execution on very large datasets.

Many believe that artificial intelligence is a futuristic concept that we only see in sci-fi movies with humanoid robots and holograms. However, it is becoming rooted in our reality, affecting various fields and groups, including persons with disabilities. Accessibility and inclusivity are genuinely revolutionized, thanks to artificial intelligence! People with disabilities can substantially enhance their daily life thanks to AI technology solutions. We've already shown how smartphones can be tools for people with vision impairments.

In this modern era of the digital world where people are trying to find simplicity and ease in doing works, moreover, these new inventions are booming out for making mankind a way to lead a simple life using different technologies and ways among that one is Artificial intelligence or shortly known as AI a progressively increasing field of study which having its touch in every area starting from social media to e-commerce websites for a good recommendation of posts of our type and products based on search recommendations along within the different domains like agriculture, virtual assistance, shopping recommendations and for physically disabled people, security, stock market prediction, self-driving cars and many more. This modern era of AI can visualize things and understand real-time around us and can make optimal decisions and remove real-time problems moreover for the efficient work in addition with the reduction of human errors. How Ai is useful for disabled persons? Nowadays everyone has a mobile phone or any electronic devices, we spend more time of day with these devices and the development of this advanced technology like artificial intelligence and natural language processing, iot made their roots into our daily life's electronics and these developed features can be used in different ways where people who are suffering from disabilities can remove their problems or inconvenience related easily without any help of other person using apps and software's developed by advance technology. There is a different type of disabilities people face and mainly with Vision, Hearing, Prosthetics are three kinds mostly we can observe in the world, different technologies are present for vision-related issues such as braille displays and writing and many more for a long time, and in recent times AI has changed this thing and improved the way of interacting.

For any company these days, the Recommendation system has become a vital part, every company wants to give a personalised experience to the user and for that Recommendation, systems are the best choice. LET'S UNDERSTAND WHAT IS A RECOMMENDATION SYSTEM… Let's say you want to buy a t-shirt from Amazon, you went to their website and type black t-shirt, You will see some Black T-shirts on your screen, Simple right??? Now let's say you liked some t-shirts on the first page and went inside to see them, lets say you select the third t-shirt from the left (BLACK PANTHER ONE), you checked its reviews, ratings, etc. Now you came back to the first page and select some different black t-shirts let's say with a round collar or maybe t-shirts with a particular brand etc. Now if you pay attention, Amazon is collecting every information, every click of yours, whenever you are going to a particular brand or particular pattern, Amazon has started to know your likings, disliking. It is the same like let's say you have gone to the nearest market to shop for a t-shirt with a new friend, the new friend did not know anything about your liking or disliking and he is just observing you, he is noticing every action of yours, What patterns you are choosing?? What brands you are choosing?? What color are you opting for?? Amazon is that unnecessary friend who is keeping a watch on you every time you are buying something on its website.

Recommendation systems are a key modern application of machine learning, but they have the downside that they often draw upon sensitive user information in making their predictions. We show how to address this deficiency by basing a service's recommendation engine upon recommendations from other existing services, which contain no sensitive information by nature. Specifically, we introduce a contextual multi-armed bandit recommendation framework where the agent has access to recommendations for other services. In our setting, the user's (potentially sensitive) information belongs to a high-dimensional latent space, and the ideal recommendations for the source and target tasks (which are non-sensitive) are given by unknown linear transformations of the user information. So long as the tasks rely on similar segments of the user information, we can decompose the target recommendation problem into systematic components that can be derived from the source recommendations, and idiosyncratic components that are user-specific and cannot be derived from the source, but have significantly lower dimensionality. We propose an explore-then-refine approach to learning and utilizing this decomposition; then using ideas from perturbation theory and statistical concentration of measure, we prove our algorithm achieves regret comparable to a strong skyline that has full knowledge of the source and target transformations. We also consider a generalization of our algorithm to a model with many simultaneous targets and no source. Our methods obtain superior empirical results on synthetic benchmarks.

It is very difficult to quantify how much impact does data science and software engineering has on our lives. Most of us can hardly remember the dark age of just few years ago , where you couldn’t ask Siri for the directions to the nearest restaurant. If you do remember that time, you probably wouldn’t wanna go back. Today you don’t have to drive around in your car just to find a restaurant to have a nice meal, instead you can ask your mobile assistant (developed by software engineer), which will trigger an algorithm (developed by data scientist) and will show you the location of nearest restaurant on you phone map application (developed by software engineer). And this is only regarding our personal lives. These technologies have made a much bigger impact on industries. Using software and big data, businesses are able to make data-informed decisions. This means being better able to identify an audience, anticipate their needs, give them what they want, and make bigger profits. What is data science? Data science is hard to define exactly, but you could think of it as “the use of algorithms and statistics to draw insights from structured and unstructured data”.  The goal of a data scientist is going to depend quite a lot on the problem they’re examining. From organizations trying to meddle with petabytes of data, a data scientist’s role was to help them utilize this opportunity to find insights from this data pool. They will use their computer science, statistics, and mathematical skills to analyze, process, interpret and store data. In order to uncover useful intelligence for their organizations, data scientists must master the full spectrum of the data science life cycle and possess a level of flexibility and understanding to maximize returns at each phase of the process. What is software engineering? Software engineering has two parts: software and engineering. Software is a collection of codes, documents, and triggers that does a specific job and fills a specific requirement. Engineering is the development of products using best practices, principles, and methods. So, software engineering is defined as a process of analyzing user requirements and then designing, building, and testing software application which will satisfy those requirements.  In software development, the goal is to create new programs, applications, systems, and even video games. Because there’s no such thing as bug-free software, an inescapable secondary goal for software engineers is to constantly patch and iterate on existing software to make it better and ensure it performs as required. Behind every software products there are so many stages involved, which all are done by software engineers. Data Science V/S Software Enginnering Both Data Science and Software Engineering domains involve programming skills. Where Data Science is concerned with gathering and analyzing data and Software Engineering focuses on developing applications, features, and functionality for the end-users. Data Science Software Engineering Data Science focuses on gathering and processing data. Software Engineering focuses on the development of applications and features for users. Includes machine learning and statistics. Focuses more on coding languages. Deals with Data Visualisation tools, Data Analytics tools, and Database Tools. Software Engineering deals with programming instruments, database services plan instruments, CMS devices, testing devices, integration apparatus, etc. Deals with Exploratory Data. Software Engineering focuses on systems building. Data Science is Process Oriented Software Engineering is methodology-oriented. Skills include programming, machine learning, statistics, data visualization. Skills include the ability to program and code in multiple languages. Data science deals with data and prediction and it is often not obvious what a software engineer has to do with this data-centric or data-driven team. This is because: A software engineer in a data science team is only an engineer with a knowledge of data; A data scientist knows mathematics and statistics to understand the problem and the product; They also know programming languages to build the model So the question arises, how is software engineering important for data science or what does a software engineer brings in the data science team, and here is the answer: Importance of Software Engineering Software Engineer plays an important role when it comes to productization of data science application by adding hardware, enhancing the performance, so that the data science work can be provided to external customers. Some of the responsibilities are: Building APIs: Data scientist converts the models to APIs that can be easily used by other applications but a Software engineer has to ensure that the APIs created from the model is scalable, flexible and reliable. They also use the models built by data scientists and tests and deploys them.  Model Examination: The final product relies totally on the software engineer. They has to make sure that the model made by the data scientist can be used as a common model and that it can be easily managed. By easy management, it means that they has to make sure that the model can be easily moderated to suit the other product requirements as well. For this reason, they need to be updated with all the changes made in the code. Model testing and deploying: Any model, big or small, complex or easy, made by data scientists must be tested. His job is to review the code or the model created by the data scientist. Unit testing, branch testing, integration testing, security testing of the model is a part of his job. After testing, they take a decision to deploy the model. And now since all the software requires basic data like customer needs, famous functionalities, etc. , data scientist are becoming an important part of software development team as well. So we can safely say that both are dependent on each other and completes each other.

API and artificial intelligence have only one thing in common. They are both very old technologies that have been revamped in recent years to see a phenomenal upsurge in their adoption. The field of artificial intelligence is an interdisciplinary field that is both related to computer science and data science. By using artificial intelligence APIs, it is much easier to gather or collect data from various sources. Data can be transformed into different formats by applying different algorithms to it using machine learning.

Artificial Intelligence has enabled us to perform our daily jobs in new and more efficient ways. AI can assist people with disabilities by significantly improving their ability to get around and participate in daily activities by automating the process that would typically need human intellect, such as speech recognition and several other functions. Artificial intelligence (AI) has the potential to change the lives of individuals with disabilities by facilitating the development of interactive technologies that promote accessibility and flexibility. For persons with impairments, AI-assisted voice-assist devices like Google Home, Alexa have opened new possibilities of accessibility. Because Artificial Intelligence is so important in communication and engagement, it makes it much easier for persons with disabilities to access information simply by speaking to their devices.

Nowadays, recommender systems already impact almost every facet of peoples lives. To provide personalized high quality recommendation results, conventional systems usually train pointwise rankers to predict the absolute value of objectives and leverage a distinct shallow tower to estimate and alleviate the impact of position bias. However, with such a training paradigm, the optimization target differs a lot from the ranking metrics valuing the relative order of top ranked items rather than the prediction precision of each item. Moreover, as the existing system tends to recommend more relevant items at higher positions, it is difficult for the shallow tower based methods to precisely attribute the user feedback to the impact of position or relevance. Therefore, there exists an exciting opportunity for us to get enhanced performance if we manage to solve the aforementioned issues. Unbiased learning to rank algorithms, which are verified to model the relative relevance accurately based on noisy feedback, are appealing candidates and have already been applied in many applications with single categorical labels, such as user click signals. Nevertheless, the existing unbiased LTR methods cannot properly handle multiple feedback incorporating both categorical and continuous labels. Accordingly, we design a novel unbiased LTR algorithm to tackle the challenges, which innovatively models position bias in the pairwise fashion and introduces the pairwise trust bias to separate the position bias, trust bias, and user relevance explicitly. Experiment results on public benchmark datasets and internal live traffic show the superior results of the proposed method for both categorical and continuous labels.

Machine learning and artificial intelligence are often used interchangeably, but they are two very different things. Artificial Intelligence (AI) is a broad field that has been around for decades -- it's the technology behind Siri and Alexa. Machine Learning (ML), on the other hand, is a subset of AI that uses statistical techniques to allow computers to learn without being explicitly programmed with rules or instructions. Therefore, you can see that machine learning and artificial intelligence aren't interchangeable terms; rather they're related fields in computer science that both utilize mathematics and statistics to create intelligent behavior from machines. To better understand the difference between the two, think of it this way: AI is a toaster that can make toast; ML is an algorithm where you tell the toaster how dark you want your toast and when.