Artificial intelligence-enabled drones developing company Skydio secures a contract worth $20.2 million per year with the United States Army. Skydio will now equip the U.S. army with high-end artificial intelligence-powered drones to increase the Army’s battle strength and capabilities.
The deal is a part of the U.S. Army’s Short Range Reconnaissance (SRR) program, in which the Army is looking to develop and deploy cost-effective and lightweight drones. Skydio plans to provide the U.S. Army with its Skydio X2D drones as a part of this contract.
The Army’s Short Range Reconnaissance program aims to deliver a rucksack portable, vertical take-off and landing drone that provides the Soldier on the ground the capability to gain situational awareness.
Col.Joseph Anderson said, “The selection of the U.S. Army’s short-range reconnaissance provider for tranche 1 is a significant milestone for the Army, our strategic partners, and the domestic industrial base. The future for our Soldiers is now.” He further added that their partnerships with industry reflect their willingness to compete as well as their ability to lead and innovate in unmanned systems technology.
According to Skydio, their X2D drone has a dimension of 11.9″ x 5.5″ x 3.6″ when folded, and 26.1″ x 22.4″ x 8.3″ when unfolded, making it extremely portable.
The drones are equipped with front-facing 4K cameras having 16x zoom ability and an infrared camera. The company claims that its drone has a flight time of 35 minutes and can operate 24 hours, regardless of the lighting conditions.
Skydio X2D drones are packed with artificial intelligence-powered tools that enable 360-degree obstacle avoidance. United States-based AI drones manufacturing firm Skydio was founded by Abraham Bachrach, Adam Bry, and Matt Donahoe in 2014. The company specializes in developing drones that recognize and avoid obstacles in real-time using a variety of cameras and unique computer vision technology.
CEO and Co-founder of Skdio, Adam Bry, said, “This is an exciting milestone for Skydio, the Army, and most importantly the men and women who serve our country. For drones under 20 pounds, civilian drone technology has raced ahead of traditional defense systems.”
Indian Astronomers have used a new artificial intelligence-powered technology to accurately discover 60 earth-like habitable planets in the universe. The new artificial intelligence technology helped astronomers in finding 60 potentially habitable planets from a total of 5000 planets identified.
According to the astronomers, the artificial intelligence algorithm used to identify planets intensively scanned multiple planets to find out which planets have similar characteristics like Earth, making them potentially habitable.
The AI solution named the Multi-Stage Memetic Binary Tree Anomaly Identifier (MSMBTAI) is built on a revolutionary multi-stage memetic algorithm (MSMA). Astronomers from the Indian Institute of Astrophysics, an autonomous institute of India’s Department of Science and Technology, Government of India, and astronomers from BITS Pilani, Goa campus.
Prof. Snehanshu Saha from BITS Pilani Goa Campus and Dr. Margarita Safonova of the Indian Institute of Astrophysics led the research team in this new discovery.
A statement from the Ministry of Science and Technology mentioned, “The method is based on the postulate that Earth is an anomaly, with the possibility of the existence of few other anomalies among thousands of data points…There are 60 potentially habitable planets out of about 5000 confirmed and nearly 8000 candidate planets proposed. The assessment is based on their close similarity to Earth.”
It also stated that these recently discovered planets can be thought of as anomalous examples among a vast pool of ‘non-habitable’ exoplanets. This unique approach developed by astronomers from IIA and BITS Pilani is based on the assumption that Earth is an anomaly, with the possibility of a few other anomalies among thousands of data points.
Nevertheless, it is still difficult to accurately identify such plates given the massive number of exoplanets that require a long time. The artificial intelligence-powered solution made it easier for astronomers to analyze thousands of planets manually and identify potentially habitable planets.
The World Health Organization (WHO) recently published a report highlighting the benefits and dangers of artificial intelligence for older people. Many sectors, including public health and medicine, are being transformed by artificial intelligence.
The technology can aid in predicting health risks and occurrences and medicine discovery and personalization of healthcare management. Though AI has innumerable benefits, it also comes with multiple concerns and risks if precautions are not taken.
Older people may find it challenging to contribute to the appropriate governance and oversight of AI technology for health. The design and reach of artificial intelligence-powered products and solutions can also be limited by false assumptions about how older people want to live or engage with technology in their daily lives.
WHO proposed eight principles in the new document, including participatory design of AI technology by and with older people, age-diverse data science teams, age-inclusive data gathering, and many more.
Ethical challenges for healthcare institutions, practitioners, and recipients of medical and public health services must be addressed in order to fully enjoy the benefits of artificial intelligence. Unit Head of Demographics and Healthy Aging at WHO said, “To ensure that AI technologies play a beneficial role, ageism must be identified and eliminated from their design, development, use, and evaluation. This new policy brief shows how.”
She further added that in this discipline, societal implicit and explicit biases, especially those related to age, are frequently duplicated. It is of prime importance that AI-powered solutions that affect day to day lives of elderly people do not worsen or promote ageism. According to WHO’s document, ageism has far-reaching effects on all aspects of health, well-being, and the economy.
Technology and electronics company Sony’s subsidiary Sony AI announces a breakthrough in artificial intelligence called Gran Turismo Sophy. Sony Ai collaborated with Polyphony Digital Inc. (PDI) and Sony Interactive Entertainment (SIE) to develop this newly launched technology.
Gran Turismo Sophy is the first superhuman AI agent to beat the world’s greatest drivers in Gran Turismo 4’s incredibly realistic racing simulation game. GT Sophy is an autonomous AI agent that was trained using a revolutionary deep reinforcement learning platform built by Sony AI, PDI, and SIE jointly.
According to experts in video game racing and artificial intelligence, GT Sophy’s accomplishment is a significant milestone, with the agent demonstrating mastery of tactics and strategy.
Chairman, President, and CEO of Sony, Kenichiro Yoshida, said, “This group collaboration in which we have built a game AI for gamers is truly unique to Sony as a creative entertainment company. It signals a significant leap in the advancement of AI while also offering enhanced experiences to GT fans around the world.”
He further added that the company wants to fill the world with emotion through the power of creativity and technology. The newly developed artificial intelligence technology promises to provide players all across the world with unique AI-powered gaming experiences.
Sony AI was founded on April 1, 2020, with the purpose of using artificial intelligence to liberate human imagination and creativity. Sony AI’s newly launched technology was able to reach extraordinary performance on three tracks after 45,000 hours of training.
“Gran Turismo Sophy is a significant development in AI whose purpose is not simply to be better than human players, but to offer players a stimulating opponent that can accelerate and elevate the players’ techniques and creativity to the next level,” said the CEO of Sony AI, Hiroaki Kitano.
He also mentioned that they hope that this breakthrough will open up new potential in fields such as autonomous racing, autonomous driving, high-speed robots, and control.
The government of India is planning to use artificial intelligence solutions to tackle the emerging challenge of power distribution losses across the country.
The government is now reaching out to major information technology companies and startups to help them develop AI-enabled solutions to handle this issue.
This new development falls under the government’s Rs 3.03 lakh crore reform-based and result-linked scheme, with a corpus of Rs 4 crore sanctioned for the first year by the electricity ministry.
Distribution loss is one of the most prevalent issues that India’s energy sector has been facing in recent years, and this initiative of the government would considerably help solve the problem. According to reports, the country witnessed a high transmission and distribution loss of more than 20% in 2019, one of the world’s highest.
Power Secretary Alok Kumar said, “Huge data will be thrown up when we implement smart meters in a time-bound manner. We are conscious that this data should be analyzed intelligently in a way that it leads to good actionable points for the utility managers and for the policymakers.”
He further added that the average distribution loss in India is 20%. However, many utilities experience losses of 40-45 percent, with a few companies losing more than 50%.
According to the government, the developer of the solution will leverage technologies including artificial intelligence, machine learning, and IoT to analyze data made available through consumer, transformer, and feeder metering.
The government plans to select multiple technology providers, companies, and startups to tackle the power distribution issue in the country. A government official said, “Increased technology interventions will aid in facilitating operational and financial sustainability of the distribution companies.”
The government will allow the selected companies to make judgments on loss reduction, demand forecasting, differential tariff in a day, and renewable energy integration using innovative technologies.
Lawmakers of the United States claim that artificial intelligence-powered facial recognition system developing company Clearview AI could possibly end public anonymity if the federal government does not ditch it.
Recently, democratic senators are stepping up their efforts to limit the federal government’s collaboration with the infamous surveillance company Clearview AI. Clearview Ai has been into multiple controversies over the years regarding its practices that violate citizens’ right to privacy.
Despite all criticism, the company was awarded a US patent for its one-of-a-kind technology earlier this month. Lawmakers said, “In conjunction with the company’s facial recognition capabilities, this trove of personal information is capable of fundamentally dismantling Americans’ expectation that they can move, assemble, or simply appear in public without being identified.”
The lawmakers demanded that the Departments of Justice, Defense, Homeland Security, and the Interior stop using the company’s technology through multiple letters sent on Wednesday.
According to them, Clearview Ai poses a massive threat to the security of citizens regarding their privacy. Senators Ed Markey and Jeff Merkley, along with Representatives Pramila Jayapal and Ayanna Pressley, signed the letters.
Lawmakers suggest that Clearview AI’s collaborations with government agencies are particularly concerning because citizens would start to believe that their government is spying on them. Hence, they would be less likely to engage in civic discourse or other activities protected by the First Amendment.
United States-based artificial intelligence-powered facial recognition solution developer Clearview Ai was founded by Hoan Ton-That and Richard Schwartz in 2017. The firm specializes in providing a research tool primarily used by several law enforcement agencies to identify perpetrators and victims of crimes. To date, the company has raised more than $38 million over three funding rounds from investors, including Kirenaga Partners, Hal Lambert, and many more.
CEO and Co-founder of Clearview AI, Hoan Ton-That said, “We are proud of our record of achievement in helping over 3,100 law enforcement agencies in the United States solve heinous crimes, such as crimes against children and seniors, financial fraud, and human trafficking.”
A study suggests that the Machine Learning market size is expected to grow from $8.81 Billion by 2022, at a CAGR of 44.1%. Machine learning is a subset of artificial intelligence that focuses on developing systems that can learn without human supervision or assistance. Python as a programming language focuses on code readability, functionality, and scalability, making it the most preferred language for developing machine learning models. Machine learning models require continuous data processing, and Python libraries for machine learning such as pandas, TensorFlow, Keras, NLTK, etc., help developers access, handle, analyze, and transform data.
Python, a general-purpose programming language, was released in 1991 and designed to optimize the code readability. It’s ranked #2 in the list of best programming languages by Ubuntu Pit. One of the features that make Python stand out as the best programming language is that it’s open-source and has an extensive set of libraries. These inbuilt libraries can be used for data mining, data manipulation, and machine learning.
This article will cover the top 10 Python libraries for machine learning:
SciPy
SciPy is the top Python machine learning library for scientific and analytical computing. It contains different modules for linear algebra, integration, special functions, signal and image processing, Fast Fourier transform, Ordinary Differential Equation (ODE), optimization, statistics, etc., other computational tasks in science and analytics. The multi-dimensional array provided by NumPy is the underlying data structure that SciPy uses for array manipulation subroutines. It is also perfect for image manipulation.
SciPy comes with various sub-packages that offer functions and tools for interpolation, linear algebra, signal processing, algorithms for nearest neighbors, convex hulls, numerical integration routines, etc.
Scikit-learn, an extension of SciPy, is one of the most popular machine learning libraries for classical ML algorithms. It is used for data mining and analysis, making it an excellent tool for developers starting their ML journey. Scikit-learn is built on two basic Python libraries: NumPy and SciPy. It supports most of the supervised and unsupervised learning algorithms, providing an easy and robust structure that helps ML models learn, transform, and predict with the help of data.
Scikit-Learn provides various functionalities that help create classification, regression, and clustering models for applications like preprocessing, model assessment, statistical analysis, and much more. It has a consistent, easy-to-use interface that is suitable for designing pipelines. However, Scikit-learn is heavily dependent on the SciPy stack, and it can’t employ categorical data to algorithms.
Theano
Theano is one of the popular machine learning libraries in Python that enables users to define, evaluate and optimize mathematical expressions with the help of multi-dimensional arrays. Developers use it to detect and diagnose errors with unit-testing and self-verification. However, it’s more efficient on GPU to perform complex computations than CPU.
Theano is a powerful Python machine learning library partly because of its integration with NumPy. Due to this integration, it can be used in large-scale computationally intensive scientific projects. However, Theano has a steep learning curve, and it’s comparatively slower in the backend.
TensorFlow
TensorFlow, one of the best Python libraries for machine learning, was developed by the Google Brain team at Google for high-performance numerical computations. It’s one of the best open-source Python libraries for machine learning that involves defining and running computations involving tensors. Various startups and companies since have started using TensorFlow in their technology stacks. It is a flexible ecosystem community and tools that allow, in general, to build and deploy machine learning-powered solutions. With TensorFlow, companies can put their models in production mode in the cloud or on-premises and the browser or on-device.
It can visualize ML models using TensorBoard and implement reinforcement learning. However, its computational graphs are comparatively slower when executed.
Keras
Keras is a Python library used in machine learning that provides an interface of TensorFlow Library focused on neural networks that can also run on CNTK and Theano. It is a user-friendly library that allows fast and easy prototyping and can run seamlessly on both CPU and GPU. Keras is a portable framework that also provides multi-backend support.
Keras is among the best Python libraries for machine learning that is highly compatible with other third-party tools, libraries, and low-level deep learning languages. This Python library for machine learning has tools like neural layer, objectives, batch normalization, dropout, and pooling for creating a neural network.
PyTorch
PyTorch is an open-source, popular machine learning library for Python based on Torch; an open-source ML library implemented in C with a wrapper in Lua. It’s one of the Python libraries for machine learning that comes with an extensive choice of tools that support Natural Language Processing (NLP), Computer Vision, and many more ML programs. PyTorch allows developers to perform computations on Tensors with accelerated processing via GPU acceleration and it’s easy to integrate with the rest of the Python ecosystem. Features such as distributed training and hybrid frontend are reasons for Pytorch popularity. It’s also famous for its quick execution speed and the capability of handling powerful graphs.
NLTK
NLTK or Natural Language Toolkit is one of the Python libraries used in machine learning to work with natural language processing in Python. This library supports various text processing such as tokenization, software removal, stemming, POS tagging, classification, lowercase conversion, etc. It is a suite of programs and libraries for statistical and symbolic natural language processing for the English language. NLTK is one of the Python libraries for machine learning that can also be used for analyzing reviews, text classification, sentiment analysis, text mining, etc. NLTK offers a wide range of linguistic resources such as WordNet, Word2Vec, and FrameNet. However, NLTK can only split text by sentences and can’t analyze the semantic structure. In addition, it doesn’t support neural network models.
Pandas
Pandas is a popular Python machine learning library that provides high-level data structures and a wide variety of tools for data analysis. It was developed specifically for data extraction and preparation. It also provides various inbuilt methods for data manipulation such as groping, combining, iterating, integration, reindexing, and filtering. It uses DataFrames, a handy and descriptive data structure, to create models for implementing functions. Pandas also provide data writing and reading using sources such as HDFS and Excel. It can be implemented in a wide range of areas like education and business because of its optimized operation.
It supports operations such as Aggregations, Re-indexing, Concatenations, Iteration, Sorting, and Visualizations. One of the outstanding features of this top Python machine learning library is translating complex data operations using one or two commands. Pandas have many inbuilt methods for grouping, combining, and filtering data. However, it has a very steep learning curve and poor 3D matrix compatibility.
PyCaret
PyCaret is a top Python machine learning library that is open source and low code. It is an end-to-end ML and model management tool that increases the efficiency of an experiment cycle and increases productivity. With PyCaret, developers can replace hundreds of lines of code with a few lines, making experiments exponentially faster and more efficient. It allows the model to be evaluated, tuned, and compared to a given data set with just a few lines of code.
NumPy
NumPy is one of the top machine learning Python libraries that Keras and TensorFlow use to implement operations on tensors. It is an interpreter and interactive library that can execute complex mathematical operations on extensive multi-dimensional data in a simple manner. It also offers features like discrete Fourier transformation, basic linear algebra, sorting and selecting capabilities, and support for n-dimensional arrays.
NumPy has tools for integrating Fortran, C, and C++, making it one of the most popular Python libraries for machine learning among the scientific community. It has a massive community of programmers who share experiences and help developers resolve issues. However, the major drawback is that the data types are not Python native, increasing cost when entities have to be translated back to Python relevant entities.
Conclusion
In this blog, you learned the best Python libraries for machine learning. Each machine learning Python library has its functionalities, features, and disadvantages. While Keras allows fast calculations and prototyping, Scikit-learn is used for basic ML algorithms like regression, classification, clustering, etc. NLTK is the top Python machine learning library for natural language processing, and TensorFlow works with deep learning to train and employ artificial neural networks. You should take the functionalities and routines of each library into account before selecting the suitable Python machine learning library for designing your models.
Artificial intelligence and machine learning company Actable AI raises $1.2 million in its pre-seed funding round led by London-based venture capital firm, Begin Capital.
Other investors like Charlotte Street Capital, Malta Enterprise, and several more from the United Kingdom, Singapore, and the United States also participated in the funding round.
Actable AI wants to use the newly raised funds to make data analytics more accessible for one billion spreadsheet users across the world.
Actable AI allows spreadsheet users with no prior knowledge of statistics and programming to analyze data using advanced AI-based analytics directly in Google Sheets and Excel. Additionally, the company plans to launch its Google Sheets Add-on, Excel Plugin, and several other plugins this year.
Partner at Begin Capital, Alex Menn, said, “The computing power and learning ability of software may fundamentally disrupt the role of experts. Various software applications will enable an average worker to replicate the skills of a professional. Actable AI is standing at the intersection of two beloved VC trends: the rise of new professions and AI no-code solutions.”
He further added that the Begin Capital team is delighted to assist the founders at this early stage of their firm, and they are very optimistic about the future prospects. Actable AI intends to democratize a wide range of analytics jobs, making them available to everyone, everywhere, rather than just data experts.
United Kingdom-based artificial intelligence company Actable AI was founded by Armen Poghosyan and Trung Huynh in 2020. The firm specializes in providing a cloud-based, no-code, powerful data analytics platform.
Actable AI’s platform enables millions of analysts to quickly clean and analyze their data using our cutting-edge AI and deep learning technologies without having to program.
CEO and Co-founder of Actable AI, Armen Poghosyan, said, “We are really excited about this funding as it will allow us to continue to grow and bring advanced analytics to companies all around the world. It will also help us to democratize the data science market, making it easier for SMEs and business professionals to use their data to tackle real-world issues.”
World’s one of the most popular battle royale games PUBG’s developer Krafton plans to build new artificial intelligence-powered virtual humans. This is the company’s first attempt at entering the metaverse environment.
Krafton will concentrate on developing lifelike virtual avatars for usage in games, eSports, and as virtual influencers and singers. Krafton plans to use hyperrealism character manufacturing technology to build digital avatars of humans.
To further improve the communication capabilities of virtual humans, the company intends to leverage various technologies, including text-to-speech, speech-to-text, voice-to-face, and artificial intelligence.
Creative Director at Krafton, Shin Seok-jin, said, ”We are geared up for realizing an interactive virtual world (Metaverse) in stages and will continue to introduce more advanced versions of virtual humans and content based on the belief in the infinite scalability of such technologies.”
According to the company, its virtual human will come with multiple realistic features like motion-captured dynamic movements, pupil movements, a wide range of facial expressions, and skin hair. Recently, Krafton announced it invested $2.5 million in Seoul Auction Blue and $4.1 million XBYBLUE.
Additionally, the company also signed an agreement to develop non-fungible token (NFT) oriented projects. Krafton CEO CH Kim earlier said that the business would actively harness new technology to provide unique experiences to gamers and creators. As the consent of Metaverse is gaining popularity, multiple companies across the globe are making their moves to tap into this new technology.
Krafton says that digital humans will play an essential role in the Metaverse, representing real people in the virtual world. At first, virtual avatar customization choices would be limited to outfits and skins. The company believes that as Metaverse becomes more accessible to people, the demand for virtual avatars will skyrocket.
Financial markets data and infrastructure providing company Refinitiv announces the launch of its new AI assistant, Refinitiv AI Alerts, in Microsoft Teams for delivering impactful market insights.
The company has partnered with human-centered artificial intelligence company ModuleQ to develop the new Ai assistant. Refinitiv AI Alerts empower financial professionals with personalized, timely, and actionable market insights.
ModuleQ’s patented algorithms and Refinitiv’s Intelligent Tagging service create user-specific content suggestions and alerts, which are then linked back to Refinitiv Eikon and Workspace for further analysis and action.
Refinitiv AI Alerts asks for permission to learn the user’s specific priorities from their Microsoft 365 interactions, keeps that information private, and suggests content based on planned meetings and frequent email chats.
“We welcome partner solutions such as Refinitiv AI Alerts, which combine the best of market-leading data, AI, and workflow to provide our mutual customers with even more value from our relationships,” said Corporate VP of Worldwide Financial Services at Microsoft, Bill Borden.
The tool can seamlessly provide professionals with a competitive edge in their research and consumer interactions. London Stock Exchange Group’s subsidiary Refinitiv was founded by David Craig in 2018. The firm specializes in providing insights, trading platforms, and open data and technology platforms for the finance industry. Refinitiv has a customer base of more than 40,000 institutions spread across 190 countries worldwide.
Group Head of Data and Analytics at London Stock Exchange Group, Andrea Remyn Stone, said, “Refinitiv AI Alerts brings critical content and insights to Refinitiv’s customer base within this platform, with the goal of allowing users to discover and act on timely information across Teams, Refinitiv solutions and Microsoft 365 seamlessly.”
She further added that Microsoft Teams has become a must-have platform for workers in the financial services industry, and institutions are quick to adopt it. Therefore the newly launched Refinitiv AI Alerts will be very beneficial for Teams users.
