The rise of machine learning (ML) and its embedding in systems has drastically changed the engineering of software-intensive systems. Traditionally, software engineering focuses on manually created artifacts such as source code and the process of creating them, as well as best practices for integrating them, i.e., software architectures. In contrast, the development of ML artifacts, i.e. ML models, comes from data science and focuses on the ML models and their training data. However, to deliver value to end users, these ML models must be embedded in traditional software, often forming complex topologies. In fact, ML-enabled software can easily incorporate many different ML models. While the challenges and practices of building ML-enabled systems have been studied to some extent, beyond isolated examples, little is known about the characteristics of real-world ML-enabled systems. Properly embedding ML models in systems so that they can be easily maintained or reused is far from trivial. We need to improve our empirical understanding of such systems, which we address by presenting the first large-scale study of real ML-enabled software systems, covering over 2,928 open source systems on GitHub. We classified and analyzed them to determine their characteristics, as well as their practices for reusing ML models and related code, and the architecture of these systems. Our findings provide practitioners and researchers with insight into practices for embedding and integrating ML models, bringing data science and software engineering closer together.

AutoFarm releases AutoLabs to research, develop and integrate AI into AutoFarm's products We are thrilled to announce that Autofarm, the leading lowest fee multi-chain DEX & yield aggregator protocol, is set to integrate advanced Artificial Intelligence (AI) and Machine Learning (ML) technologies to revolutionise yield generation and scalability on the platform. AutoFarm has established AutoLabs, an in-house research division dedicated to exploring the integration of advanced AI/ML technologies. A specialised AI architecture team, comprising experts from various AI backgrounds, has been assembled within AutoLabs to lead this initiative. The goal of this internal research division is to empower Autofarm's products with the ability to analyse real-world data dynamically, identify profitable opportunities, and make autonomous decisions for optimal asset allocation across multiple blockchain networks. One key technique that Autofarm plans to implement is the use of oracles to bridge on-chain and off-chain data.

The exceptional progress in the field of machine learning (ML) in recent years has attracted a lot of interest in using this technology in aviation. Possible airborne applications of ML include safety-critical functions, which must be developed in compliance with rigorous certification standards of the aviation industry. Current certification standards for the aviation industry were developed prior to the ML renaissance without taking specifics of ML technology into account. There are some fundamental incompatibilities between traditional design assurance approaches and certain aspects of ML-based systems. In this paper, we analyze the current airborne certification standards and show that all objectives of the standards can be achieved for a low-criticality ML-based system if certain assumptions about ML development workflow are applied.

They can also be used for financial fraud. Fraudsters can use deepfake technology to trick employees at financial institutions into changing account numbers and initiating money transfer requests for substantial amounts, says Satish Lalchand, principal at Deloitte Transaction and Business Analytics. He notes that these transactions are often difficult, if not impossible, to reverse. Cybercriminals are constantly adopting new techniques to evade know-your-customer verification processes and fraud detection controls. In response, many businesses are exploring ways machine learning (ML) can detect fraudulent transactions involving synthetic media, synthetic identity fraud, or other suspicious behaviors.

Machine learning (ML) is one of the hottest and most lucrative tech trends. According to a survey on the state of AI conducted by McKinsey in 2021, 67 percent of companies that adopted AI-related technologies saw increases in revenue. Increased adoption of ML technology has given rise to some of the best machine learning startups, all of which are leading the digital transformation in the 21st Century. These machine learning startup companies are located around the globe, including San Francisco, Santa Clara, San Jose, San Mateo, Redwood City, and the rest of Silicon Valley, as well as places like London and Tel Aviv. This article will explore exciting startups in the private sector and public sector, looking at their innovative ideas, funding, and expected growth.

Artificial Intelligence (AI) and Machine Learning (ML) can be used to tackle crucial issues like climate change and carbon emissions, which could bring humanity one step closer to achieving our sustainability goals. However, increased use of AI and ML technologies can also have an impact on greenhouse gas emissions which means creating a sustainable form of these technologies is key to the wider picture of climate consciousness. Recently, a group of researchers led by Professor Lynn H. Kaack at Berlin's Hertie School published a paper in the journal Nature Climate Change investigating how AI and ML technologies may impact greenhouse gas emissions – both positively and negatively – and what measures can be taken help to align AI/ML policy with climate change goals. The aim of the study is to establish how the emissions from AI/ML activities can be quantified in order to better understand how the increasing use of these technologies is influencing the climate. Climate change should be a key consideration when developing and assessing AI technologies.

AI and ML technologies diversify the lending ecosystem seamlessly, efficiently, and effectively. The digitalized world we live in has enabled individuals and businesses to grow and keep ahead of their competition. Many mobile lending apps have exploded in India in recent years due to the increasing accessibility of smartphones. The government encouraged digitization in banking which resulted in financial technology (Fintech), firms racing to fill the gaps, especially in the category of digital loans. Disruptive technologies such as Artificial Intelligence and Machine Learning are gaining popularity in nearly every industry. The financial sector is also a beneficiary of large amounts of data.

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

Machine Learning trends continue to enthrall analysts all over the world. It's true that AI adoption is on the rise but the real push into 2022 comes from the evidence-based approach to building a more sustainable and human-friendly environment. According to a recent report, 56 percent of the global business leaders have stated AI adoption in at least one function. Leading AI companies such as NVIDIA, Microsoft, Google, Amazon Web Services (AWS), Alibaba.com and Tencent Cloud made remarkable investments in the last 2-3 years to develop cutting-edge capabilities in AI Machine Learning, data science, and automation domains. Today, artificial intelligence (AI) and machine learning (ML) trends have become the key barometers for industrial development, enabling organizations to progress toward becoming the most disruptive and revolutionary enterprises in the world.

Google CEO Sundar Pichai said that the impact of AI would be even more significant than that of fire or electricity on the development of humans as a species. It may be an ambitious claim, but AI's potential is very clear from the way it has been used to explore space, tackle climate change, and develop cancer treatments. Now, it may be difficult to imagine the impact of machines making faster and more accurate decisions than humans, but one thing is certain: In 2022, new trends and breakthroughs will continue to emerge and push the boundaries of AI and ML. Here are the top eight AI and ML trends to watch out for in 2022. Since the advent of AI and ML, there have always been fears and concerns regarding these disruptive technologies that will replace human workers and even make some jobs obsolete.