As the end of 2022 draws near, it is time once again for Creative Virtual's annual year in review blog post. Every year we take this opportunity to reflect on the hard work of our team, our contributions to the conversational AI industry, and a few of our company's biggest highlights from the past 12 months. Two of the things we are proudest of at Creative Virtual are our experienced, dedicated team and the unique expertise we provide to our customers and partners. Whether it's through our product development or our collaborations with individual clients, it's important to us that we consistently deliver the best solutions possible. Having an analyst group recognise us for this is always an exciting bonus – and that's what happened again this year.

As a service-based industry, the travel industry achieves its success on the experience level of customers. AI chatbot for travel industry enhances traveler experiences by delivering quick virtual support services. Bots can be used to provide travelers with a simple overview of flights and hotels available based on their criteria and booking option. A bot that can have both travel data and destination content would become a perfect travel companion. The travel bot would provide business travelers with information about their journey step by step.

Laird Connectivity, a global leader in wireless modules, internal antennas, IoT Devices, and custom wireless solutions, is pleased to announce it has acquired California-based Boundary Devices, a leader in designing and manufacturing System-on-Modules (SOM) and Single Board Computers (SBC). Founded in 2003 and headquartered in Lake Forest, California, Boundary Devices is a pioneer and leader in providing innovative SOM and SBC products that serve a diverse and global customer base across high-growth end markets, including IoT, commercial equipment, laboratory instruments, and industrial automation. The Company provides a one-stop-shop destination for customers seeking a total-solutions partner able to offer: hardware design for NXP Semiconductors i.MX applications processors, software development, U.S. based manufacturing, and integration, all backed by best-in-class engineering and customer support. Recommended AI: How is Artificial Intelligence (AI) Changing the Future of Architecture? The acquisition significantly enhances the growing Laird Connectivity SOM portfolio by providing a full complement of SOM and SBC products for a wide range of customer applications.

AMD announced that Japanese automotive systems supplier Aisin is using its Xilinx Automotive (XA) Zynq UltraScale MPSoC platform for its Automated Parking-Assist (APA) system. The highly adaptable platform enables the next-generation Aisin APA system to detect pedestrians, vehicles and free space efficiently and at extremely low latency. The Aisin APA system will begin shipping in model year 2024. The Aisin APA system uses four cameras and 12 ultrasonic sensors mounted on the vehicle to recognize the surrounding environment and calculate the driving route. The system then controls the vehicle according to the calculated route to park itself.

Alex Svinov is the CEO and Co-founder of Insquad, the platform to build remote development teams. He believes that the future of work is in remote teams – and this notion will radically change the world as it will bring opportunity and talent closer to each other. Alex launched Insquad after facing challenges in hiring senior tech talent for his previous startup. He tried staffing services, but they were expensive and did not give a lot of value to him as a startup. So he decided to solve this problem and help the startup community as well as offer great opportunities to the talent in underprivileged countries. Alex is a serial entrepreneur and angel investor -- 10 investments in IT companies all over the world -- Forbes council member and Alchemist mentor. In the past 10 years, he has created several successful startups in industry areas that he had no experience in before -- FinTech, outsourcing, HRTech, and food service. He is passionate about making new tech products and services and helping distributed teams achieve their goals. Alex met with Bill Clinton and Queen Elizabeth in Moscow's high school. Outside of work, he's a father of 3, plays tennis and regularly participates in amateur tournaments. Today I have with me, Alex Svinov. Now, Alex is the CEO and Co-founder of Insquad, which is a platform to build remote development teams and as the world basically circulates around technology these days, it's very very important to get a great development team and remote now as we know with the pandemic has produced change the way we work. He believes that the future work is in remote teams and this notion will radically change the world as well bring opportunity and talent closer to each other. Alex launched Insquad one year ago because he faced challenges hiring senior tech talents for his previous startup. He tried staffing services but they were expensive and did not give a lot of value to him as a startup.

The graph represents a network of 7,073 Twitter users whose tweets in the requested range contained "(Artificial Intelligence) OR #AI", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 08 December 2022 at 05:38 UTC. The requested start date was Thursday, 08 December 2022 at 01:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 1-hour, 5-minute period from Monday, 05 December 2022 at 23:17 UTC to Thursday, 08 December 2022 at 00:23 UTC.

Machine learning, artificial intelligence, natural language processing, deep learning, robotics, and several other technologies have enabled businesses to leverage human intelligence and evaluate inputs for maximum accuracy and precision. For example, you now have image recognition software that acts as a scanner and finds the best search options on Google after interpreting what the image is. So, the application is based on ML, natural language processing, and artificial intelligence. It imitates a human who uses the item or object through the eyes and interprets the results in mind. Although all these disruptive technologies are individually the best in their field, combining them, it's a challenge.

The University of Adelaide and MTX Group have entered into a research collaboration to develop new insights in machine learning (ML) and artificial intelligence (AI). Bringing together their academic research and commercial expertise and experience, the two organisations will undertake specific, outcomes-focussed research. They will use AI to model uncertainty with a view to avoiding failure within systems that may be used in defence and business environments. The University and MTX Group have jointly been awarded $100,000 under the Artificial Intelligence for Decision Making Initiative which is a collaborative project between the Australian Government's Office of National Intelligence (ONI) and the Defence Science and Technology Group (DST). Dr Duong Nguyen and Dr George Stamatescu from the University's School of Computer Science will work alongside Dr Ammar Mohemmed from MTX Group.

If you want to be a successful data scientist or AI engineer, you must master the various deep learning frameworks that are currently available. In this article, we'll enlighten you about the best option for data scientists. TensorFlow and PyTorch both provide valuable abstractions that make model creation easier by minimizing boilerplate code. They vary in that PyTorch takes a more "pythonic" approach and is object-oriented, whereas TensorFlow provides a wide range of possibilities. PyTorch is used for many deep learning projects today, and its popularity among AI researchers is growing, despite being the least popular of the three main frameworks.

Just by browsing the internet, you may have heard or seen of fascinating apps and websites that can generate a seemingly random face of a person within a span of seconds. You may even have tried some out to see what types of faces can be generated. Are the faces faces of real people? A lot of these platforms develop a GAN (General Adversarial Network) machine learning model and train it on a dataset made of real human faces so this GAN can then learn these differing human features to generate new human faces. Therefore, most of the times, the generated faces you see on the internet or on an app are not real, meaning you won't be able to trace any photo back to someone in this world.