Altair, an AI company providing software and cloud solutions in high-performance computing (HPC), data analytics, and AI, has invested $10M in Xscape Photonics. Xscape is a startup that has developed patented photonic chip technology for ultra-high bandwidth connections inside computing systems and data centers.
James R. Scapa, founder, and CEO of Altair stated that the investment and collaboration with some of the world’s best innovators in Photonics would enable Altair to focus on advanced technologies to help customers to solve their problems more efficiently,
Until now, computing relied on traditional electronic ways to transport vast amounts of data from chip to chip. This resulted in a lot of space and power and generated a lot of heat from the computer systems. Therefore, it caused performance issues in high-performance computing applications, especially in AI and data science applications.
Xscape has created a platform that integrates diverse computing elements in an environmentally sustainable way while providing the maximum possible performance using breakthrough photonics technology. The use of photonics cuts power consumption and heat output while increasing communication speed and power in applications.
Alexander Gaeta, CEO and co-founder of Xscape Photonics mentioned that Xscape is proud to re-invent the future of computing by developing the highest bandwidth, energy efficiency, and high-performance photonics technology to meet the needs of the future. The investment and collaboration with Altair will enable Xscape to push the boundaries of its platform and integrate with the best HPC and AI software to help customers in all sectors.
Diver X, a Japanese VR startup that pitched HalfDrive VR Headsets earlier this year, has launched a new Kickstarter campaign for a pair of Diver X VR haptic gloves that contain flexing and compressing membranes to mimic touch sensations.
The HalfDrive Kickstarter fame saw the light in January as the campaign secured enough cash to be fully funded. However, the Diver X team decided against it and returned the funds as the device that clearly took inspiration from Sword Art Online failed the scalability test.
Now, the company is back with another Kickstarter campaign with ContactGlove, a pair of Diver X VR haptic gloves that tracks fingers and positions with SteamVR and offers input emulation via buttons.
It is up to the user to decide if and when to use this function because button input is an emulated process in which configuration software links certain buttons to hand motions, such as bending your right index finger to pull a trigger.
The “pro” function on higher-end versions also offers haptic feedback by contracting and expanding to mimic touch on the user’s fingertips. As per the company, the VR haptic gloves are compatible with SteamVR and come with mounting adapters for Tundra Trackers and Vive Trackers.
The Diver X VR haptic gloves are available for pre-order on Kickstarter, which already seems to have caught fire in the landscape. The project has now surpassed its original funding goal of US$200,000. The VR haptic gloves start at US$490 for models without the touch membrane and US$710 for those with Tundra Trackers. The haptic versions start at approximately US$870.
Carter, a London-based generative AI startup, secures £1.7m in pre-seed funding round to use conversational AI for background video game characters. The funding round was led by Play Ventures, Connect Ventures, and a group of angel investors: Chris Lee, Steve Chard, GFR Fund, Jas Purewal, Affan Butt, and Rupert Loman.
Carter is working on conversational AI to help game developers realistically develop computerized gaming characters. The company is developing an AI toolkit to allow developers to integrate conversational AI to create game characters speaking in local languages.
Danial Ali, the co-founder of Carter, stated that computerized gaming characters could bring unconditional love and friendship through the human-to-machine relationship. According to him, creating realistic video game characters is a big goal and might sound like sci-fi, but there is a standard to the idea of such a computerized community.
Established in April 2022, Carter follows the vision of building meaningful relationships between humans and digital companions. It allows developers to build more meaningful conversations in their games and projects. Carter uses different techniques enabled by AI, inspired by human behavior and the industry’s leading research. The founders of Carter, Ali, and Huw Prosser have exclusive experience in the AI field. They have founded the business automation software company Bloomware previously.
The German automotive company Mercedes-Benz paves the path to enter the metaverse by filing for five NFT trademarks for its line. Michael Kondoudis, a licensed attorney, revealed the trademark in a tweet that showed the car manufacturer’s application with the United States Patent and Trademark Office (USPTO).
#MercedesBenz has filed trademark applications for: ▶️ Mercedes Benz ▶️ Mercedes ▶️ S-Class ▶️ G-Class ▶️ Maybach
As per the update, the Mercedes trademarks are filed for Mercedes Benz, S-Class, G-Class, Maybach, and Mercedes. The applications included goods and services like computer programs featuring textiles, beauty products, fragrances, food & drinks, trading cards, parasols, and audio/visual devices for online and virtual use. The Maybach filing additionally described plans for crypto-collectibles featuring animal furs, carpets, rugs, etc.
The trademark applications also mention plans to include financial services catering to digital currencies or tokens via a global computer network, digital currency exchange services, liquidity services for digital currencies, and blockchain assets.
The Mercedes NFT trademarks come nearly a year after Mercedes Benz unveiled its first NFT project in January, wherein Art2People collaborated with five artists to compile their digital renditions of the Mercedes-Benz G-Class.
In May, the carmaker joined the Aura Blockchain Consortium of Luxury Brands as a founding member, providing access to read-to-use NFT and blockchain technology.
OpenAI introduced its new open-source AI generator that generates 3D models based on text prompts. Point-E, a machine learning system, differs from other traditional 3D generators because it uses discrete data points to represent 3D shapes rather than create them.
3D modeling is a highly applicable technology in movies, video games, AR, VR, metaverse, etc. However, producing photorealistic 3D graphics still requires a lot of resources and effort, and doing so using text prompts is a further achievement.
Taking inspiration from the recently viral text-to-image systems like DALL-E, Lensa, and HuggingFace’s Stable Diffusion, Point-E attempts to enhance text-to-3D technology. Point-E, or Point Efficiency, uses point clouds as they are easily synthesized in terms of computational requirements. Unlike existing systems like DreamFusion, Point-E does not require hours of GPU functions. However, its resolution is not that great.
OpenAI’s research team, led by Alex Nichol, said, “Other systems leverage a large corpus of (text, image) pairs, allowing it to follow diverse and complex prompts, while our image-to-3D model is trained on a smaller dataset of (image, 3D) pairs.”
When prompted with a text, Point-E first creates a synthetic 3D rendering. It will then run this version through a series of diffusion models to generate a 3D, RGB, 1024-point cloud model. The next step generates a finer version of the same, with 4096 points. Each of these diffusion models was developed using “millions” of 3D models that had all been transformed into a standardized format.
This wireless electro-tactile system uses a skin-friendly hydrogel layer that sticks onto the palm of the hand and collects personalized tactile sensing data to bring a more realistic virtual touch experience to the metaverse.
The WeTac system developed by CityU consists of two parts: a palm patch with hydrogel electrodes as a tactile interface and a tiny flexible actuator that acts as a control panel. The whole actuator weighs only 19.2 grams and is small enough to be worn on one’s forearm.
It also has Bluetooth Low Energy (BLE) and a tiny rechargeable lithium-ion battery for wireless transmission and power. The thickness of the palm patch is a mere 220 microns to 1 mm, and the electrodes reach from the palm to the fingertips.
Through this, users will be able to experience objects in virtual scenarios, such as grasping a tennis ball during sports practice or touching a cactus in virtual social networks or games.
A coastal province called Zhejiang, in China is planning to build a US$28.7b industry metaverse by 2025, clubbing multiple companies together to facilitate technology development. The province presented the plan on December 15 as a part of its efforts to become one of the largest metaverse hubs.
Over the last few years, many Chinese provinces have expressed interest in developing metaverse-oriented technologies and making the country a metaverse hub. Recently, it was reported that the Chinese metaverse industry raises US$780m in funding and is expected to become a US$5.8 trillion industry in the coming decade.
In the presented document, Zhejiang authorities outline the idea and actions starting in 2023. One of these is the incubation of 50 startups, ten industry leaders, and other metaverse-related essential technologies, such as blockchain, virtual reality (VR), and artificial intelligence (AI). These technologies will bring companies together in production processes, industrial design, and governance.
Zhejiang’s document is not the only metaverse proposal. A few other local governments in China have also expressed their ideas outlining similar plans for developing a metaverse. In June, Shanghai also presented an industry metaverse roadmap for US$52m.
ImagenAI, a startup that uses artificial intelligence (AI) for photo-editing and automating media production processes, raises US$30m in an all-equity growth investment from Summit Partners. The investment will further the startup’s Saas (software-as-a-service) offering.
ImagenAI, not to be confused with Google’s Imagen, was founded by Yotam Gil, Ron Oren, and Yoav Chai in 2020. The idea rose from stealth after Chai’s wedding, wherein he had to wait months to get the wedding pictures and videos. After speaking with photographers, the founders identified a significant industry issue: post-production is “tedious and time-consuming.”
Imagen is available as a cloud plugin for Adobe Lightroom Classic and an independent app designed to learn the photographer’s style from their previous work. It uses machine learning (ML) to capture the editing process and make predictions of editing parameters within half a second at US$0.05 per photo.
Gil said, “Imagen profiles evolve and learn with the user over time, allowing better accuracy and consistency in applying each photographer’s style to new photos ingested into Imagen.” He added that the service would benefit photographers who edit at scale.
ImagenAI also provides pre-trained profiles based on industry experts. These profiles, called Talent AI Profiles, have pre-determined editing styles so that users can directly optimize their clicks without setting up editing parameters.
ImagenAI is making over US$10m in year-wise recurring revenue and will be profitable in the near future as the company plans to innovate using the all-equity investment for services like automatically picking the best set of pictures from a shoot and a lot more.
Helm.ai, a California-based startup developing software designed for advanced driver assistance systems, recently raised $31 million in a Series C funding round led by Freeman Group, only one year after it secured $26 million in venture funding.
Its partners, including Amplo and strategic investors Honda Motor, Goodyear Ventures, and Sungwoo Hitech, have pushed Helm.ai’s valuation to about $431 million.
Founder of the Freeman Group, Brandon Freeman, is joining Helm.ai’s board of directors as part of this financing. The company has raised $78 million to this date.
The six-year-old startup, Helm.ai, uses an unsupervised learning method to develop software that can train neural networks without the requirement for large-scale fleet data, simulation, or annotation.
Helm.ai provides its software to various Tier 1 suppliers and OEMs in the automotive industry to help them achieve software differentiation with high-end ADAS and L4 solutions.
The recent funding will add more employees to the 50-person workforce, R&D, and will help towards establishing several commercial partnerships.
Over the last few years, most enterprises have undergone a digital transformation and produced unimaginable volumes of data. This raw data is insufficient to push data science projects forward in production. As per Gartner, back in 2017, 85% of data projects failed because the data could not be trusted to facilitate business decisions. Gartner predicted these results because earlier data scientists were expected to work on the data before actually using it in the project. However, it has become apparent that “someone” needs to organize and transform this data to ensure quality, usability, and availability so that data scientists do not spend much time before the actual work begins. Data engineers are the ones who get this job done. You can opt for a data engineering course to learn more about data engineering and get one of the most in-demand jobs in the big data world.
What do data engineers do?
A data engineer’s primary objective is to transform the raw data into something valuable and understandable before presenting it to an enterprise. In addition, they must design, construct, test, mix, manage, and refine the data using various tools and sources. The goal is to build data pipelines that operate efficiently. Additionally, data engineers work closely with the infrastructure teams to automate several steps in the data engineering procedures. In addition to all of this, they create challenging queries to make the data available.
Top Data Engineering Courses
Several data engineering courses are available, and selecting the right one is challenging. This article has enlisted some knowledgeable courses for a data engineer. Have a look.
Professional Certificate in Data Engineering Fundamentals (IBM)
Professional Certificate in Data Engineering Fundamentals (IBM) is an excellent introductory data engineering course if you are interested in venturing into data engineering. Since data engineers are the core of a data science project as they create pipelines guiding the workflow, it becomes inevitable not to know the fundamentals. This course provides a comprehensive theoretical and practical introduction to building pipelines, managing data, and engineering work ecosystems to lifecycles.
The certification includes three sub-courses:
Data Engineering Basics
Python Basics for Data Science
Relational Databases and SQL.
The course will span over 4 months and take an average of 4-6 hours per week.
Data Engineering with AWS Machine Learning (Pluralsight)
Storing data for complex machine learning projects is tedious because of varying data formats. This data engineering course focuses on how to store data and leverage machine learning on the AWS platform. In this course, Data Engineering with AWS Machine Learning by Pluralsight, you will learn how to select the appropriate AWS service for each data-related activity for any given scenario. Initially, you will investigate data storage options and the purposes of each type of storage. Finally, you will learn to transform raw data into usable formats.
The course will cover several topics that will introduce you to data engineering with AWS.
Typical Data Flow for ML on AWS
Database Storage Options for ML on AWS
Data Warehouses and Data Lakes
Batch Data Ingestion
Data-driven Workflow
It is a short course that you can finish within 3 hours and will bring you one step closer to using AWS machine learning services with ease.
Data Engineering Learning Path – Coursera
Data Engineering Learning Path is an excellent umbrella course offered by Coursera with which you can learn essential skills that a data engineer needs. Coursera suggests a combination of sub-courses that will aid you in moving towards a full-fledged data career. The following courses are recommended for a data engineering learning path:
Business Intelligence Analyst – Power BI, Tableau, SQL
Business Intelligence Developer – Software development, SQL, Javascript
Data Engineering – Python, Big Data, ETL
Coursera recommends a Coursera Plus subscription to guide you through multiple courses in a career learning path, with access to over 3000-course options.
Become a Data Engineer: Mastering the Concepts – LinkedIn Learning
If you are looking for a data engineer course online, LinkedIn Learning offers an extensive beginner-level course, Become a Data Engineer, for those who wish to learn the fundamentals of data engineering from scratch. You will study the core principles of data engineering, DevOps, trade-related tricks, and how to use them in platforms for project work. The course discusses Big Data, SQL, and NoSQL coding for analysis. Moving forward, you will understand how Apache Sparks work with Big Data technologies.
The course will cover
Data Science Foundations
NoSQL Essentials
Apache Spark Essential Training
Architecting Big Data Applications
Cloud SQL and SQL Essentials
Advanced NoSQL for Data Science and SQL Professionals
It will take approximately 13 hours to cover the entire material, and you will get a certificate on completion.
Data Engineering – ETL, Web Scraping, Big Data, SQL, Power BI (Udemy)
If you are looking for a big data engineer course, Data Engineering – ETL, Web Scraping, Big Data, SQL, Power BI is a beginner-level data engineering course that will teach you how to interact with data. It covers ETL, Web Scraping, SSIS, SQL, and Big Data.
The crash course is divided into twelve sections covering 134 video lectures covering the following topics:
ETL, or Extract, Transforms, and Load, a data pipeline using which people can extract data from several sources, transform it according to the requirements, and load it in a data store.
Secondly, you will also learn about SQL Server Integration Services for data integration, transformation, and solving business problems.
Big Data, including numbers, audio, images, text, and other kinds of data with high volume, variety, and velocity.
You will become familiar with SQL, a standard programming language for managing databases.
Lastly, you will learn Power BI, a robust business analytics solution that helps with data visualization and business insights.
The course content is about twelve hours long and can be completed flexibly. On completion, you will be able to implement ETL with SSIS, scrap web data with Python, Beautiful Soup, and Scrapy, connect web data with Power BI, and model with Power BI.
Professional Certificate in Data Engineering (IBM)
After learning data engineering fundamentals, proceeding with another course, like Professional Certificate in Data Engineering by IBM, will be a significant next step. This is one of the best data engineer course in India, designed for people who want to advance their interest and knowledge in the field. It advances the basics while teaching you application development, more complex pipelines, and data warehousing.
The course is divided into 14 sub-courses that will give you an insight into cloud-based relational databases (RDBMS) and NoSQL databases. Some of these are:
Python for Data Engineering
SQL for Data Engineers
Building ETL and Data Pipelines
Big Data Engineering, Hadoop, and Spark Basics
Data Engineering Capstone Project
The course spans over one year and two months, with an average of 3-4 hours per week. On completion, you will have acquired skills in Hadoop, Big Data, PostgreSQL, Bash, Data Warehousing, and other related technologies.
Microsoft Azure Data Engineering Associate DP-203 Exam Prep Specialization
It is not a standard course like other data engineering courses. However, opting for Microsoft Azure Data Engineering Associate Exam Prep Specialization will give you a different insight into data engineering. It is a rewarding path to being an associate with Microsoft, where you will learn about basic theoretical concepts and get hands-on experience with real-world scenarios.
The specialization program will cover the following sub-courses:
Data Engineering with Microsoft Azure
Data Storage and Integration
Data Warehousing and Engineering
Preparation for Data Engineering on Microsoft Azure Exam
It will take approximately thirteen months to complete, with an average of two hours per week. On completion, you learn about Azure Synapse Analytics, Apache Spark, Modern Data Warehousing, Azure Data Lake Storage, and other related technologies.
A data engineer must know at least one cloud service provider and its services. Amazon Web Services (AWS) is an industry leader in cloud computing. Data engineers acquainted with an AWS Certified Solutions Architect – Associate (SAA) have better chances at career profiles and high earnings. In this intermediate-level course, AWS Solutions Architect Associate Certificate Prep, you will get expert guidance on how and what to prepare for the examination.
The first week talks about multi-tier data solutions and storage technologies. The following week talk about flexible and scalable computing solutions and database networks. In week three, you will learn how to secure your data and database network. Lastly, the fourth week will teach you computing and database services cost optimization.
The month-long course comes with flexible deadlines, sample certification questions, and skill-based hands-on exercises on data structures and architectures.
Taming Big Data with Apache Spark and Python
This Big Data engineering course, Taming Big Data with Apache Spark and Python on Udemy, focuses on Big Data analysis using Apache Spark and Python. With more than 20 hands-on examples with large data sets, you will learn to use DataFrames, structured streaming with Spark 3, and MLLib for ML-driven data mining and other related concepts. The course is divided into eight sections, covering 66 video lectures. These sections are structured to cover the following concepts:
Introduction to Spark and RDD interface
SparkSQL, DataFrames, and DataSets
Spark Clusters and Spark ML
Spark Streaming and Graph X
The course will take approximately seven hours, with access to a personal Windows/Linux computer and some prior scripting experience.
Data Structures and Algorithms Nanodegree (Udacity)
In this data engineering course, Data Structures and Algorithms Nanodegree from Udacity, you will be acquainted with more than 100 data structures. Data engineers should know their way around multiple data structures and algorithms to be proficient in managing and sorting data. Knowing about data structures also makes them capable of understanding patterns in data and deciding appropriate operations. During the course, industry experts will deliver online lectures on Udacity’s platform and provide personalized project reviews. Once you finish the course, your project will undergo a strict review process to get certified.
The certification will cover three sub-courses:
Data Structures
Basic Algorithms
Advanced Algorithms.
You need to have a basic knowledge of Python and Algebra to enroll in the course over 4 months, with an average of 10 hours per week.
