Diffusion Bee is a Mac app that creates AI images with sound diffusion using an open-source AI image generation framework. The app, developed by Divam Gupta, can be installed on M1/M2 running at least macOS 15.2. It utilizes Apple’s Silicon’s built-in AI and runs the Stable Diffusion model.
Introducing Diffusion Bee, the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.
Stable diffusion is a text-to-image machine learning model that enables image generation from input texts. Until now, text-to-image was only possible via MidJourney or DALL-E, initially only if you were to make the waitlist.
Besides DALL-E and MidJourney, an open-source, free-to-use Stable Diffusion tool is also accessible on GitHub or Hugging Face. While using the open-source stable diffusion tool is a feasible option, it requires you to follow a step-by-step guide to install all the requisites. Mac’s Diffusion Bee app makes it a little more convenient. You just have to drop the app in your applications folder, open click, and write your prompts.
The app enables you to tweak image height and width, the number of steps the model runs, and the importance the model gives to your text prompts. There is no fee, and it does not require any additional software/hardware connection except the internet.
The developers recommend 16GB RAM due to the memory-intensive nature of machine learning, but it works very typically with 8GB RAM without any glitch.
Following President Joe Biden’s March 9 executive order, the white house has released a comprehensive framework for the responsible development of digital assets, especially crypto. Over the last six months, the whole-of-government approach has been focused on mitigating risks and harnessing the benefits of digital assets.
Several agencies have collaborated to develop frameworks and policies with six key priorities: financial stability, managing illicit finance, US leadership in global finance, economic competitiveness, financial inclusions, and responsible innovation.
Nine comprehensive reports, consistent with the orders’ deadlines, have been submitted to the President. These reports suggest agencies promote innovation by indulging in private-sector research and development. The reports also call for the Central Bank Digital Currency (CBDC) risk mitigation methods by experimentation and evaluating a “Treasury-led interagency working group.”
Digital assets come with a significant risk for consumers, businesses, and investors due to price volatility. The Biden-Harris administration and regulators have been working to ensure fair play in the financial and digital assets market. The administration has issued guidance and pursued the fraudulent agents to protect consumers.
Some significant steps that have been taken:
Securities and Exchange (SEC) and Commodity Futures Trading Commission (CFTC) to enforce actions against unlawful practices.
Consumer Financial Protection Bureau (CFRB) and Federal Trade Commission (FTC) to monitor consumer grievances.
Financial Literacy Education Commission (FLEC) to lead public awareness to help consumers understand the risks of digital assets.
Office of Science and Technology Policy (OSTP) and NSF will develop a Digital Assets Research and Development Agenda to initiate advancements.
NSF will cover social sciences and the training of diverse groups of stakeholders.
The Department of Commerce will establish a forum to convene agencies, academics, and civil society to exchange debates on standards, rules, technical requirements, and research support.
You can check the official press release to know more about the framework.
The discipline of statistics concerns the collection, organization, interpretation, analysis, and presentation of data. The practice of applied statistics, which is the root of data analysis, involves the analysis of data to help better define and determine organizational needs. Today, applied statistics and its applications are employed in various fields, including information technology, medicine, finance, engineering, marketing, accounting, business, etc.
Famous statisticians like Prasanta Chandra Mahalanobis, C.R. Rao, and Debabrata Basu have contributed profoundly to the field of statistics. India has been a major hub for statistical education and research for decades. This article lists some of the top statistics institutes in India. However, the numbering is not necessarily the institute ranking or an order of preference.
Hindu College, New Delhi
Hindu College is one of the most popular statistics institutes in India. Course offerings include BSc and MSc in statistics. The college is a constituent of Delhi University and was established in 1899, making it one of India’s oldest offering statistics courses. The statistics department at Hindu College was the first in its league at the undergraduate level at Delhi University. The statistics department has committed faculties who specialize in various academic fields related to statistics, including stochastic modeling, inference, biostatistics prediction theory, etc. Besides regular teaching, the faculty is also involved in research projects and publishing books.
Lady Shri Ram College for Women, New Delhi
Lady Shri Ram College for Women is counted among the top statistics institutes in India. The Bachelor of Science (B.Sc.) program in the department of statistics offers an exciting space to study the quantitative aspects of the biological, social, and physical sciences. The courses focus on the crucial role of statistics in diverse areas and its vitality in finance, marketing, and strategy-making. Students are given the training to acquire tools in the fields of statistical methods, applied statistics, and analysis. The courses also teach students rigorous methods and techniques to sift through a plethora of data and comment on it in a skilled manner. The emphasis of the teaching-learning process at LSR College is mainly on relating statistical concepts to real-world examples that are familiar to students.
Loyola College, Chennai
Loyola College, one of the top statistics colleges in India, is affiliated with the University of Madras. Loyola College’s statistics department was established as an undergraduate department in 1959. The department was upgraded to a postgraduate department in 1982 with the introduction of the MSc Statistics program. The department further advanced to a research department with the introduction of the M.Phil. and Ph.D. programs in statistics. The statistics department faculty is well qualified to teach allied and elective courses and provide consultancy services. The department has witnessed many outstanding students with university rankings. Loyola college is a private higher education institution that is run by the Society of Jesus in Chennai.
Madras Christian College, Chennai
Madras Christian College (MCC) is another name on the list of top statistics colleges in India. Course offerings include BSc, MSc, PhD, and MPhil in Statistics. The college is affiliated with the University of Madras, although it functions as an autonomous institute from its main campus in Chennai. The statistics department at MCC was established in 1969. However, the undergraduate course in statistics was started back in 1962. The department offers courses with a comprehensive combination of computer applications, statistics, operational research, and managerial economics. The department has a well-equipped laboratory named Gift Siromoney Statistical Computing Laboratory. One of the salient features of the statistics department is the practical training provided to the students through the conduction of socio-economic and tribal surveys and public opinions.
Presidency College, Chennai
Presidency College is one of the oldest statistics institutes in India. The department of statistics of the college is one of the pioneering departments in the south of India. The college introduced the undergraduate and postgraduate programs in statistics in 1960. The intake of students at present for UG is 24 and 18 for PG. Currently, the college is also offering research degrees, including MPhil and PhD. During the last five decades, the statistics department has produced several eminent personalities in both professional and academic domains. The research scholars and teachers of the department have made notable contributions in several areas of research, multivariate data analysis, including quantitative management, sample surveys, quality assurance, model building, etc.
Indian Statistical Institute, Kolkata
Indian Statistical Institute (ISI), Kolkata, is one of the premium statistics institutes in India. It was created out of the statistical laboratory established by Prasanta Chandra Mahalanobis in Kolkata’s Presidency College. Established in 1931, this esteemed institution is one of India’s oldest institutes focused on statistics. Indian Statistical Institute offers varied undergraduate, postgraduate and doctoral degree programs. The bachelor’s and master’s degree programs in statistics at ISI, commonly known as BStat and MStat, are flagship programs of the college that are acclaimed internationally. Research activities at the institute are organized under several broad disciplines. Research scholars, students, faculty members, and scientists at the institute work on a diverse range of problems in the statistics field.
Fergusson College, Pune
Fergusson College, Pune is one of the top statistics colleges in India that offers various courses in the stream of computational sciences like statistics. Course offerings include BSc and BA in statistics. The Bachelor of Science (BSc) in Statistics degree at Fergusson College is focused on the interpretation of numerical data and statistical analysis. The statistics department of the college was established in 1956. Various distinguished statisticians, including Dr.Y.S. Sathe, Dr. Badhe, Dr. Huzurbazar, Dr. B.K.Kale and Dr. A.V.Kharshikar have delivered lectures at the institute. Talks and lectures on core statistical concepts, innovative topics in statistics, and their applications in diverse fields are regularly held by the Statistics Association of the college.
IIT Kanpur
IIT Kanpur is another name on the list of esteemed statistics institutes in India. The Mathematics and Statistics department of IIT Kanpur shares the vision of achieving excellence in teaching and research. The institute offers MSc and PhD programs in statistics. The courses in statistics at IIT Kanpur are designed to cultivate mathematical interests, motivate research in mathematical sciences, cultivate mathematical taste, and train computational scientists to enable them to work on challenging real-life problems. The department’s faculty have written more than 40 research monographs and textbooks. Moreover, the research contributions made by the facilities of the statistics department are noteworthy. Over the years, IIT Kanpur has evolved into one of the premier departments in the country, providing excellent teaching and research in Statistics.
St. Xavier’s College, Mumbai
St. Xavier’s College, Mumbai, is counted among the prestigious statistics colleges in India. BSC in statistics. The statistics department at St. Xavier’s was started back in the late fifties. The alums have been placed in top multinational companies in several domains, including insurance, finance, risk management, portfolio analysis, and statistical software development. St. Xavier’s College’s Mumbai campus offers BSc and BA degrees in statistics. During 1987-88, six statistics units were offered at a three-year BSc level. In 1989-90 three statistics units were introduced at the three-year BA level. The statistics department was selected for the DBT Star Scheme in the year 2017. The college is affiliated with the University of Mumbai. It also offers various undergraduate and postgraduate courses in commerce and management.
Ramjas College, New Delhi
Ramjas College is another name on the list of well-known statistics colleges in India. The college provides undergraduate and postgraduate degrees under the purview of the University of Delhi. The department of statistics offers comprehensive BSc and MSc degrees in statistics. The department of mathematics and statistics of Ramjas College boasts of its state-of-the-art laboratory and well-stocked library. Since its inception, the department has played a dominant role in the evolution of modern theories of statistics and mathematics. The department also boasts of a remarkable record of a large number of student placements in renowned companies and organizations over the past several years.
PSG College of Arts and Science, Coimbatore
PSG College of Arts and Science (PSG CAS) is one of the top statistics institutes in India. It was founded by G. R. Damodaran in 1947. The college’s course offerings include BSc and MSc in statistics. The department has a well-equipped statistical computing laboratory with 56 terminals that allow students to work independently of each other. The computer system supports the standard statistical softwares such as SYSTAT, STATA, SPSS, and R Language. The students will have ample opportunities at the college to get fully trained in using these softwares, which can guarantee immediate placement after graduation. One of the salient features of the department is the practical training, both manual and computer-based, provided by way of conducting socio-economic surveys and public opinions with NSSO.
Christ University Bangalore, Bangalore
Christ University, Bangalore, is undeniably one of the top statistics institutes in India and is well renowned for its quality education. It is a deemed-to-be university. The course offerings include BSc in statistics and actuarial science. The department of statistics at Christ University intends to provide a dynamic research environment and practical education, including excellent training in scientific data collection, data management, and data analysis methods in different areas of statistics. The department offers a profound interplay between application, computation, and theories of statistics. It provides a dynamic combination of pure and applied statistics with quality software skills, allowing students to participate successfully in professional environments by gaining knowledge.
Data science has become one of the IT industry’s most paid and recommended domains. As many companies are adopting the new way of data science applications to solve their business problems, there is a requirement for skilled data science professionals. If you want to be an expert in data science, this article will help you with some top data science books that are readily available and help you learn data science from the beginning.
Data Science from Scratch (2nd Edition)
Written by Joel Grus, The Data Science from Scratch is another exciting book for beginners to study data science. This book was first released in April 2019 and highlighted the different data science tools and algorithms necessary to implement machine learning algorithms.
If you have prior experience in mathematics and programming language, this book helps you quickly start with statistics in data science. While reading this book, you can also learn the hacking skills needed to be a data scientist. The book guides you on K-nearest neighbors, decision trees, logistic regression, clustering models, and linear regression. It also exposes you to network analysis, natural language processing, and more.
Written by Abhishek Thakur, Approaching (Almost) Any Machine Learning Problem is one of the best books for people with theoretical knowledge of machine learning and deep learning algorithms. This book focuses more on solving machine learning and deep learning real-life problems rather than explaining the basics of it.
If you want to implement deep learning and machine learning algorithms with code, this book is for you. With this book, you can explore topics like supervised learning, unsupervised learning, cross-validation, evaluation metrics, feature engineering, feature selection, arranging machine learning projects, image classification, text classification, regression, stacking, and model serving.
The Elements of Statistics Learning book by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, is the complete book for machine learning fundamentals. It covers everything from supervised machine learning methods, graphical models, unsupervised machine learning methods, and high-dimensional problems. This book includes chapters on neural networks, support vector machines, classification trees, random forests, ensemble methods, spectral clustering, and non-negative matrix factorization.
Introduction to Data Science: Practical Approach with R and Python
Introduction to Data Science: Practical Approach with R and Python, by B. Uma Maheshwari and R. Sujatha, covers all the basic data science concepts. It guides you on the insights and golden rules needed in analyzing data. This book is used as a practical guide by many engineering/science/MBA students interested in data science.
The first author of this book, Dr. B. Uma Maheshwari, is an Associate Professor of the Decision Stream at PSG Institute of Management, Tamil Nadu, India. Whereas Dr. Sujatha, the second author of this book, is an Associate Professor at PSG college of Technology in PSG Institute of Technology, Tamil Nadu, India.
Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd Edition
Written by Aurelien Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow, 2nd Edition covers the practical aspects of machine learning with efficient tools. It consists of concrete examples, minimal theory, and two production-ready Python frameworks like TensorFlow, Scikit-Learn, and Keras. With this book, you can learn techniques from simple linear regression to deep neural networks.
You need basic Python programming language knowledge to implement the algorithms included in this book. While reading this book, you will also get exposed to the topics like net architectures consisting of convolutional nets, recurrent nets, and reinforcement learning. You can also explore several training models, such as support vector machines, random forests, ensemble methods, and decision trees.
Deep Learning From Scratch: Building with Python From First Principles
The Deep Learning From Scratch: Building with Python From First Principles by Seth Weidman gives you a comprehensive introduction to data science with deep learning algorithms. With this book, you can start with the basics of deep learning and then move to advanced architectures, implementing everything from scratch.
The author explains how neural networks work using the first principles approach. You can also learn to apply multilayer neural networks, recurrent neural networks, and convolutional neural networks from scratch. While reading this book, you can understand how neural networks work mathematically, conceptually, and computationally on real-life deep learning projects.
This book explains all neural networks, code examples, and mathematical expressions in simple language. With this book, you can implement neural networks using the popular PyTorch framework.
Written by Garrett Grolemund and Hadley Wickham, R for Data science is an exciting book for beginners to learn data science with R. With this book, you can learn to get your data into R, transform it, visualize it, and model it accordingly. The book also explains the concepts of statistics with other data science processes like data cleaning and filtering. It also guides you in data transformation with concepts like median, standard deviation, average, and more.
The book helps you understand the raw data and how it is processed. You can also learn the different methods for transforming data and processing it to gain meaningful insights using R with the help of this book.
Introduction to Machine Learning with Python: A Guide for Data Scientists
Introduction to Machine Learning with Python, written by Andreas Muller and Sarah Guido, was released in October 2016. This book is freely available on the O’Reilly platform with a 10-day free trial.
While reading this book, you can learn different practical ways to build machine learning models. This book focuses on the basics of machine learning applications with Python and the Scikit-learn library. The authors of this book have prioritized more on practical aspects of machine learning rather than the mathematics behind it. You need to be familiar with the Python libraries like NumPy and matplotlib to understand the concepts in the book more easily.
The Practical Statistics for Data Scientists book by Andrew Bruce and Peter Bruce is a good option if you are a beginner and want to master data science. This book was released in May 2017, and you can read it now on the O’Reilly platform with the 10-day free trial.
This book provides practical guidance on applying various statistical methods to data science and how to avoid their misuse. If you are familiar with the R programming language and have some statistics knowledge, you can quickly learn the concepts included in this book.
The book explains important concepts like randomization, distribution, sample bias, and sampling. It explains how these concepts are relevant in data science with examples.
Business Data Science: Combining Machine Learning and Economics to Optimize, automate, and Accelerate Business Decisions
The Business Data Science book by Matt Taddy explains the basic data science concepts from the business perspective. With this book, you can learn how data science can bring value to organizations. This book explains the fundamentals of machine learning and how to use it to solve business problems and implement it with R programming language.
Matt Taddy, the developer of the Big Data Curriculum at the University of Chicago Booth School of Business, realized that many books had the basic concepts and statistics for machine learning. Still, they lacked the proper description of machine learning concepts for solving business problems. Therefore, he introduced different tools and machine learning techniques used in businesses to solve real-life problems.
This book is available online on Amazon. It is the best option for organizations that want machine learning concepts for their business strategy.
Machine learning (ML) is a sub-field of artificial intelligence (AI) that enhances software applications’ accuracy without explicit programming changes. It uses algorithms to predict future outcomes based on historical data input. All machine learning books talk about four basic approaches to machine learning, categorized based on how an algorithm predicts. There is supervised, unsupervised, semi-supervised, and reinforcement learning.
Supervised learning: In this, algorithms are applied with labeled training data and pre-defined variables to be assessed by the algorithm. Here, both the input and the output are specified.
Unsupervised learning: In this approach, algorithms are trained on unlabeled data and try to find a meaningful connection. The data and the recommendations (or predictions) are predetermined.
Semi-supervised learning: This approach is a combination of the above two types. Here, the training data may be labeled, but even then, the model is allowed to find connections on its own.
Reinforcement learning: Reinforcement learning is used to train a machine to do a pre-defined multi-step process. The algorithms are programmed to perform a task with positive/negative cues during the process. However, the algorithm majorly decides on its own.
Many study materials are available if you wish to learn more about machine learning and algorithms. Choosing the correct reference can be tedious if you do not know what you are looking for. Here is a list of some top machine learning books and a brief description of what they offer to make your task easier.
Top Machine Learning Books
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow – 2nd Edition
If you are familiar with Python programming, this machine learning book is probably the best guiding material for understanding concepts. Written by Geron Aurelien, the book also explains the tools and frameworks required to build intelligent systems. Extra attention has been given to frameworks like TensorFlow, Keras, and Scikit-Learn. Each chapter is written in a reader-friendly manner and features exercises to apply the concepts learned in the previous chapters. You can refer to the book to hone your technical skills for projects and advance your machine learning career.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Industries like medicine, finance, marketing, etc., house much information (potential data) that can be used to analyze specific trends and patterns. However, it is challenging to understand and analyze this data. This machine learning book, written by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, discusses several tools and techniques that have surfaced to overcome the challenges of understanding data. It discusses data mining techniques, machine learning models, and bioinformatics. The book also discusses many ideas from a statistical perspective. Despite the statistical approach, the focus is maintained on the concepts, not the mathematical end.
Joos Korstanje’s machine learning book highlights the need for real-time data analysis due to evolving business and data streaming technologies. It focuses on adopting machine learning techniques to deal with data more efficiently. In the initial chapters, you will learn about the basic architecture of streaming data and real-time machine learning. The following few chapters mention a few state-of-the-art frameworks like River. In the following chapters, industrial use cases and challenges will be discussed. Once you finish the book, you will be confident in streaming data and working on your machine learning models.
The Big Book of Machine Learning Use Cases – Databricks eBook
This comprehensive guide on machine learning can give you a quick start in your career. As machine learning technologies are ever-evolving, finding more relevant real-life uses becomes challenging. With this book, you can directly jump to applying the mentioned use cases, code scripts, and notebooks. You will also learn about dynamic time warping, decision trees to detect financial frauds, and sales trends via MLflows. The book will also enable you to perform multivariate time series forecasting using RNNS (recurrent neural networks). Additionally, the ebook mentions several case studies from companies like Comcast, Nationwide, and Regeneron. With this ML book, you can instantly start using the Databricks Lakehouse Platform.
Fundamentals of Machine Learning for Predictive Data Analysis
This book, written by John D. Kelleher, is one of the best machine learning books following an analytical approach. After getting familiarized with the basic concepts of machine learning, the next step is to learn about those concepts’ practical applications and real-world use cases. Knowing about the practical use of machine learning fundamentals will help you professionally, and referring to this book will help you become proficient in predictive data analytics as it provides a comprehensive collection of ML algorithms and models.
Written by Abhishek Thakur, Approaching (Almost) Any Machine Learning Problem talks about coding and practical applications. It is the perfect book for you if you are a machine learning practitioner and do not wish to delve into theoretical concepts in detail. The book is very practical and contains many coding scripts that form the backbone of machine learning algorithms. This book is an excellent problem-solving tool if you have basic knowledge about related concepts. It explains how to set up an environment, cross-validation, evaluation metrics, feature engineering, hyperparameter optimization, and many other processes. It can be referred to as a guide with readily applicable solutions.
AI and Machine Learning for Coders: A Programmer’s Guide to Artificial Intelligence
Written by Laurence Moroney, based on his successful AI and ML courses, the book is an ideal place to start your transition from programming to becoming an AI and machine learning specialist. It is an introductory book that will offer a hands-on and code-based approach to enhancing your programming skills. The book also explains several machine learning scenarios like computer vision, sequence modeling, cloud computing, TensorFlow, and natural language processing (NLP). By the end of this book, you will be confident enough to build your own models using TensorFlow, code samples, and NLP-based tokenization and ultimately serve these models over the web via clouds.
Written by Christopher M. Bishop, this machine learning book is an excellent reference for learning and using statistical techniques to recognize patterns in data and machine learning. If you are acquainted with linear algebra and multivariate calculus, you will be able to gain a lot from this book. It leverages graphic models by describing probability distributions and features detailed practice exercises.
Applied Predictive Modeling is an introductory guide to predictive modeling and its application. Written by Dr. Kuhn and Dr. Johnson, the book will also be a treat for non-mathematical readers because it provides intuitive explanations with a problem-solving approach. The wide-ranging applications will help practitioners hone their expertise in working with accurate data. However, the book will be helpful if you have a basic understanding of statistical concepts like regression, correlation, etc. The majority of this book does not contain many complex computations, but you will need a mathematical background to understand some topics.
Machine Learning Yearning, written by Andrew Ng, provides expert guidance to ML practitioners in decision-making, data collection, debugging, etc. It introduces all necessary topics to understand machine learning and mentions real-world case studies. However, it cannot work as a guide to ML modeling as it does not contain any codes, but it will discuss all necessary background information. It is preferable for non-mathematical readers who do not want to delve into complex computations and suit those with prior experience with machine learning models but who need a more intuitive understanding.
According to a report from RBB, the municipal council of Grünheide in Germany has indefinitely delayed the vote on planned expansion of Tesla’s gigafactory in Berlin. In March, Tesla announced opening its first European Gigafactory located in Berlin.
Tesla wants to expand its almost 750-acre factory site by another 250 acres to build a freight depot, among other factory parts. The Grünheide central committee approved the plans in June and passed them to the local council to initiate the development plan.
Arne Christiani, the Grünheide mayor, removed the item from the council meeting agenda, stating the council needs more clarification and that he could not promise the plan would be voted on this year.
While it is not precisely clear what other information the local council needs from Tesla to move forward with the automaker’s expansion plans, it is possible that Russia’s efforts to turn off gas supplies to Europe in general and in particular, Germany, are preventing Christiani from approving a plan that will definitely require energy to see through.
Tesla CEO Elon Musk had previously promised that his gigafactories and electric vehicles would be as sustainably as possible. Still, it will be difficult for Tesla to avoid the factory shutdowns sweeping Germany.
The news comes days after Tesla held an information day event at the facility. During the event, the company said its production ramp-up at the factory was progressing well, without providing specific details.
Tesla is suspending plans to make battery cells in Germany as it looks to qualify for battery and electric vehicle (EV) manufacturing tax breaks in the United States (US), the Wall Street Journal reported on Wednesday. In March, Tesla had announced the opening of its first European Gigafactory located near Berlin, Germany.
According to the report, the electric vehicle maker has discussed moving equipment used to make cells to the United States, initially intended for use in the German factory.
The Inflation Reduction Act, which US President Joe Biden signed last month, allows EV makers to source batteries from within the US with tax breaks. The law also allows some buyers a $7,500 tax credit if they buy EVs whose batteries meet specific sourcing requirements.
The company is looking to boost the production of its electric vehicles at its new factories in Germany’s Berlin and Austin, Texas, to meet rising demand. The company also told Texas officials that it was scouting regional sites for a plant that would refine lithium, a key battery input that today is primarily processed in China.
The Russian invasion of Ukraine has affected the manufacturing sector by soaring energy costs in Europe. The geopolitical tension has also caused the prices of materials used in batteries to rise.
Sham Kunjur, General Motors’ executive director for EV raw materials, said discussions with outside companies to build a US battery supply chain have heated up since the law took effect.
On Thursday, Air India unveiled its future roadmap called Vihaan.AI, as part of which it plans to raise its domestic market share to 30% in the next five years. Moreover, it plans to grow international routes besides striving to reclaim its status as a global industry leader.
Air India will strive towards increasing its market share to at least 30% in the domestic market while significantly growing the international routes from the present market share over the next five years. The plan aims to put Air India on a path to sustained growth, profitability, and market leadership said Air India in a statement.
The Vihaan.AI plan includes milestones focussing on ‘dramatically’ growing both its network and fleet, developing a wholly revamped customer proposition, improving reliability and on-time performance, and taking a leadership position in technology, sustainability, and innovation, while aggressively investing in the best industry talent.
The immediate focus of the airline will be to fix the basics and readying itself for growth, while the medium-to-long term focus will be to aim for ‘excellence and establishing scale’ to become a global industry leader, according to the statement.
There are plans afoot to merge Air India with two other Tata Sons-owned airlines, viz. AirAsia India and Vistara.
Air India has made several important announcements on its flight and administrative operations in the past week. It has said that it will induct 30 new planes, including five wide-bodied, in the next 15 months and thereby increase its fleet by 25%.
It has also said that it will consolidate its workspaces to disband the regionalized organizational structure and turn it into a centralized one by moving its staff to an interim office space until they are relocated to a campus in early 2023.
Nuro, an autonomous technology developer, is collaborating with Uber Eats in a long-awaited agreement that will see the company’s newest robot take over food delivery to app customers. Just a few days ago, the two businesses agreed to a 10-year deal, opening the door for a larger rollout of Nuro’s driverless delivery robots, which have been used selectively in a number of cities.
Dave Ferguson and Jiajun Zhu, two former Google workers who worked on the self-driving car project that later became Waymo, founded Nuro in 2016. It was the first company to be granted a special exemption from several federal safety regulations, and it was also the first in California to start charging for its autonomous deliveries.
The DMV of California has granted Nuro a permit that only authorizes it to operate its delivery service in areas of the counties of Santa Clara and San Mateo, which includes much of Silicon Valley but excludes San Francisco and Oakland. That implies that the company will need further DMV approval before extending its service area.
Apart from California, Nuro recently became the first autonomous vehicle business to be awarded a commercial license in Arizona and Texas. This implies that Nuro robots can operate on public roadways for commercial purposes rather than just for research, generating revenue for the firm with each delivery.
The potential of Nuro’s delivery robots to eliminate the need for a human driver, is something that Uber has been working toward as part of its core business for years. However, the slow progress of Level 4 and Level 5 autonomy, which was generally anticipated to come around 2020, had hampered Uber’s plans, since the company has had trouble making money from its regular operations with independent contractual drivers. Early on in the pandemic, Nuro delivery robots saw a surge in interest from corporate partners, but today, rather than being seen as a more sanitary delivery partner, the company’s technology is seen as a cost-saving measure for companies like Uber Eats. Hence, it makes it logical for Uber to partner with Nuro.
The collaboration will commence with autonomous deliveries in Houston, Texas, and Mountain View, California. In the future, service will be extended to the greater Bay Area. Unlike other regions that experience extremely unpleasant weather, these two allow delivery robots to work in favorable conditions. The sensors that allow autonomous vehicles to traverse roads can become confused by rain, snow, and fog. This is also why, Nuro’s California operating license only permits it to do so during fair weather.
It is reasonable to expect that limited deployment in two cities, with intentions to debut in the Bay Area, will not bring much savings to Uber Eats. Even with an endless supply of Nuro delivery robots, regulatory approval will create the main hindrance to achieving the goal of deploying delivery robots in the long run. In addition to local and county permissions, which were challenging enough for Nuro to get in the Bay Area, where Level 4 robotaxis are being tested, commercial driverless licenses are issued state-by-state. It will be necessary for Nuro to concentrate its efforts on areas with acceptable traffic for its robots.
Uber admits that customers won’t know whether a Nuro bot is delivering their order when they place an order. Regardless of the delivery method, they will be charged the same charges. If consumers decide to tip through the app and a Nuro robot arrives in place of a human agent, they will also receive a refund for their tip. But they do have to go outside to collect their own order, and in the rain, that could be quite a nuisance.
According to the company’s video, customers would need to enter a code to access the vehicle door and get their order to demonstrate what a Nuro delivery might look like. Earlier this year, Nuro unveiled its third-generation delivery vehicle, which has an exterior airbag to save pedestrians. Because it has yet to publicly unveil its new vehicle model, it will initially employ its second-generation automobile known as the R2 for the collaboration. The R2 robot, which is around 9 feet long and 3.6 feet broad and travels up to 45 miles per hour, was unveiled by Nuro in February 2020.
The autonomous R2 is powered by renewable energy and is outfitted with 360-degree coverage with thermal cameras, lidar, and radar to traverse roadways on its own successfully. All of this is supported by specialized HD maps and collision-avoidance-based machine learning systems. Furthermore, its modest size appears to help it avoid obstacles and cause less damage in the case of an accident.
Apart from Nuro, Uber Eats has collaborated with other autonomous delivery services, viz., Motional and Serve Robotics, to test robot deliveries. In contrast to Motional, which utilizes electric Hyundai Ioniq 5 SUVs with two safety drivers seated in the front, Serve Robotics manages delivery robots that move across sidewalks. Customers in the Los Angeles region can participate in the respective pilots of Motional and Serve using Uber Eats.
Using robots and autonomous cars for last-mile delivery is something that food delivery services have long considered. For instance, DoorDash became prominent during its delivery services during the coronavirus pandemic. In June, the self-driving robots firm Cartken and Grubhub, owned by Just Eat Takeaway, announced a deal to transport groceries to college campuses.
Overall, this new alliance might be seen as the beginning of a bright new age in the autonomous vehicle industry, delivery, and transportation via EVs.
Cruise CEO Kyle Vogt said the company would add Phoenix, Arizona, and Austin, Texas, to its driverless ride service in the next ninety days. Cruise is a self-driving vehicle manufacturer that is majority owned by General Motors. Speaking at a Goldman Sachs conference, Vogt added that the loss-making Cruise unit hopes to generate US$1 billion in sales by 2025, or half of the amount of money that GM now invests annually.
In San Francisco this year, Cruise started charging for trips in its fully autonomous cars using a small number of Chevrolet Bolt electric vehicles, but only after hours to lessen the risk of mishaps as it develops the system.
Vogt stated that its activities would first be on a limited scale and “revenue-generating” in Austin and Phoenix, with an aim to build up operations next year.
In Phoenix, where it has previously tested its self-driving delivery service with Walmart, one of its investors, Cruise has all the permissions required for employing autonomous vehicles for ride-hailing and delivery services.
This comes after Cruise’s fleet of 80 self-driving vehicles had received a software update following a crash in June. According to documents published by the National Highway Traffic Safety Administration (NHTSA), one of Cruise’s vehicles was struck by an approaching Prius while making an unprotected left turn at an intersection. When the Prius shifted lanes and proceeded straight, it struck the right rear of the Cruise car, which was in the right turn and bus lane. Police determined that the other car was mostly to blame for the collision as it was going 40 in a 25 zone.
According to a report Cruise filed with the California Department of Motor Vehicles, there were apparent minor injuries to people in both vehicles, and the Cruise car sustained significant damage and had to be towed away.
However, in a subsequent filing with the NHTSA, Cruise revealed that its software operated erroneously when the car tried the famed “unprotected left” maneuver that many companies have struggled with. The Cruise car assumed that the Prius would make a right turn and that it could make its turn before the Prius arrived. It is uncertain if the Cruise was certain that the Prius wasn’t a cab or made judgments based on that knowledge. It appears like the Cruise anticipated the automobile would slow down, giving it enough time to make the turn, and started its turn. The Prius did not stop, nor did it slow down, but a sequence of errors led to the collision. It is believed that by choosing not to stop and finish its turn or by opting not to try its turn in the first place, the Cruise Bolt could have avoided the disaster.
