Artificial Intelligence is an umbrella term that covers several specific technologies. In this post, we will explore machine vision (MV) vs. computer vision (CV). They both involve the ingestion and interpretation of visual inputs, so it’s important to understand the strengths, limitations, and best use case scenarios of these overlapping technologies. The Beginning of Computer Vision Researchers began developing computer-enabled …
Can AI Help Police Officers Solve Crimes? Law enforcement is in charge of public safety, and must handle all of the challenges that come with that. Luckily, police officers are able to rely on technology for many parts of their job. In recent years, artificial intelligence in law enforcement has become an important aspect of police work globally. As AI-based …
Interoperability, or the ability for two systems to communicate effectively, is a key factor in the future of machine learning. For banking, healthcare, and other everyday industries, we’ve come to expect that the platforms we use to exchange information can communicate seamlessly whenever we need them to. Because we all have hundreds of thousands of data points associated with our …
Whether through devices we use to enable convenience at home or in the way products we use all the time are manufactured, AI’s impact is everywhere, driving innovation in just about every aspect of our lives. But there are missing pieces to this puzzle that still cause frustration for end-users and present significant challenges for researchers trying to improve how AI technology performs.
AI in E-commerce and the New Era of Personalization As society chases convenience and turns increasingly to online shopping over the traditional brick and mortar retail model, e-commerce took center stage, evolving how customers shop. The idea of a personalized shopping experience for each customer has been around for a long time, but a new era of personalization is underway. …
At Appen, we provide high-quality training data for machine learning and artificial intelligence. Follow us to stay up to date on industry trends. In this edition of our roundup, news on: applications of AI and machine learning in banking. Check back for regular news roundups and subscribe to our YouTube channel for video updates. Machine Learning in Banking: How are …
Can AI Help Police Officers Solve Crimes? Law enforcement is in charge of public safety, and must handle all of the challenges that come with that. Luckily, police officers are able to rely on technology for many parts of their job. In recent years, artificial intelligence in law enforcement has become an important aspect of police work globally. As AI-based …
2020 Predictions in Artificial Intelligence to Consider When Operationalizing AI Developments With 2019 hardly fading in the rear-view mirror, it’s a good time to take a step back and see what’s in store for the next 12 months. For those managing and implementing AI and ML projects and deployments, it’s been a rapidly evolving ecosystem, and 2020 will be no …
Traditionally, FinTech companies have been early adopters of new technologies enabling them to maintain their differentiation. Thanks to this, AI and FinTech are a natural match – especially as AI edges into the mainstream of enterprise software. At Appen, we are always looking for product-market fit when it comes to machine learning and data annotation solutions. To find out whether …
In all cases, a driverless future is coming … the question that remains is when.
Expect to see dramatic changes in both patient health outcomes and in the operational efficiency of hospitals.
What are some of the most recent developments in AI? With so many emerging applications for artificial intelligence making a splash across a wide range of industries, it can be difficult to keep up. This post will touch on some cool advances made in 2019 and look at what’s on the horizon. AI takes a deep dive Robotics is a …
Organizations should have a reliable source of clean data to extract from and work with.
This year’s O’Reilly AI conference in San Jose, CA delivered the usual mix of exciting presentations and dynamic discussions, as well as a healthy dose of industry networking. Appen is proud to have been given the opportunity to share some recent research at the conference that promises to improve the way robots interact with people. In their presentation Creating Autonomy …
Chatbots and their more sophisticated counterparts, virtual assistants, have emerged as a key tool to streamline customer service operations for organizations in a wide range of industries, and to help reduce costs.
The annual Computer Vision & Pattern Recognition conference is packed with insightful presentations and top industry experts. With nearly 10,000 attendees and 1,200 papers, CVPR 2019 in Long Beach, CA was no exception. In this post, we’ll take a brief look at some notable presentations delivered at the conference for solving numerous computer vision and pattern recognition challenges. Data augmentation …
In a recent paper entitled Optimizing the Design and Cost for Crowdsourced Conversational Utterances, Appen data scientist Phoebe Liu and her team worked to identify cost-effective crowdsourcing strategies for training dialogue systems such as chatbots. Because more industries are adopting chatbot technology for customer service and other key functions, a need has emerged for training these systems as quickly and …
When planning the development of a chatbot or virtual assistant, it’s important to begin the conversational design process with a clear data strategy in place. Conversational design requires more than just an understanding of the fundamentals of voice user interface design — you also need to understand how to solve some of the key challenges in data preparation. Working with the …
For many organizations, the temptation to annotate data for machine learning (ML) projects in-house is hard to deny. These companies typically feel that using internal resources will help them save time and money by tapping employees who are already on their payroll. Additionally, if their project is highly confidential or of a sensitive nature, they might feel that using internal …
Appen recently exhibited at AutoSens Detroit, where we shared our approach to data collection and annotation for in-car navigation, infotainment, and monitoring, as well as autonomous vehicle solutions. Here are some of the coolest innovations in automotive AI, as spotted by our team: Mercedes Benz’s Intelligent Interior Volker Entenmann, Senior Manager of UI Functions at Daimler AG, shared how Mercedes-Benz …
Machine learning (ML) algorithms are generally only as good as the data they are trained on. Bias in ML training data can take many forms, but the end result is that it can cause an algorithm to miss the relevant relations between features and target outputs. Whether your organization is a small business, global enterprise, or governmental agency, it’s essential …
For an in-car infotainment system — or any ASR system — to recognize and correctly process voice commands, it must be trained on speech data that accounts for a broad range of inputs and all possible variation in how people speak.
At the FinovateSpring 2019 conference in San Francisco, AI was a hot topic. From financial institutions using AI to power everything from personalized customer experiences to underwriting, to fintech providers showcasing AI solutions, the technology was on everyone’s mind. In a panel titled “Which Artificial Intelligence Technologies Will Really Change Financial Services?”, we heard from experts who are harnessing this technology in …
The Situation With the proliferation of mobile devices, consumers expect to stay connected, no matter where they are — especially in their car. Voice recognition in cars, however, continues to be the leading complaint among new vehicle owners. Global automotive manufacturers recognize the demand for better connectivity but face added complexities associated with localizing in-car systems for multiple languages. Data …
For simple computer vision projects, such as recognizing a pattern in a group of images, publicly available image datasets will usually suffice to train your machine learning models. But for more complex CV applications, how can you get the vast amounts of training data you need to create an accurate solution? In this post, we explain training data requirements for …
Appen recently exhibited at Google Cloud Next in San Francisco, our first event with our new colleagues from Figure Eight. Joining over 30,000 developers, product managers, data scientists, and more, we spent three days networking, learning, and problem solving. The event covered diverse topics ranging from data analytics and DevOps to networking, security, and storage. But we on the Appen …
Artificial Intelligence is an umbrella term that covers several specific technologies. In this post, we will explore machine vision (MV) and computer vision (CV). They both involve the ingestion and interpretation of visual inputs, so it’s important to understand the strengths, limitations, and best use case scenarios of these overlapping technologies. Researchers began developing computer-enabled vision technologies as early as …
Last week I attended the Deep Learning Finance conference organized by RE•WORK. As the event was smaller compared to many AI conferences I have attended, it had a more casual vibe which allowed for more open conversations about how people are using AI, some of their data challenges, and their overall goals. The insurance industry is transforming through the use …
This week, Appen is exhibiting at RE•WORK’s Deep Learning in Finance Summit in London. The summit helps business leaders, data scientists, and engineers discover advances in AI & machine learning tools and techniques from the world’s leading innovators across the financial sector. RE•WORK spoke with Appen CTO Wilson Pang to learn about his current work, the challenges of implementing AI, …
The boom in deep learning technologies such as Artificial Intelligence (AI) and Machine Learning (ML) can cause varying degrees of excitement or anxiety — largely depending on how you earn your paycheck. Advances in this field are creating exciting opportunities for companies seeking to maximize efficiency and quality, while minimizing costs traditionally associated with a mostly-human workforce. In manufacturing, implementation …
For a company to build a comprehensive dataset of annotated images takes considerable time and resources. Outsourcing the data collection and annotation efforts can help companies scale quickly.
China is now a leading global hub for AI development. Startups and major tech companies like Baidu, Alibaba, Tencent, and DiDi Chuxing are developing cutting edge products, solutions, and services using artificial intelligence. The AI market in China is massive. China’s AI Development Report 2018, a study from Tsinghua University, says, “The market value of China’s AI industry reached 23.7 …
At the 2018 AI Summit in New York City, we heard from a panel of executives representing multiple organizations across the AI ecosystem, including our own Mark Brayan, CEO of Appen. The panelists shared their perspectives on the usage of AI in different organizations, issues that need to be addressed, and what the future holds. Perspectives on AI Applications Clayton …
Appen is excited to connect with our clients and prospects in China with a fully translated site, Appen.com.cn. The launch of our full-service Chinese site will help companies explore the wide range of Appen’s training data collection and annotation solutions and services available in China. Appen expanded to China with the opening of its Beijing office in 2017. China is …
Companies across a range of industries are adopting machine learning technologies — and as early adopters, eCommerce and retail companies have seen the biggest wins from investing in machine learning. By applying artificial intelligence to key business problems, eCommerce companies are using machine learning models to drive higher sales, predict demand, and personalize the shopping experience through more relevant search …
Welcome to the fourth industrial revolution—a world where artificial intelligence (AI) drives today’s technological disruption, blurring the lines between tangible and digital realities. Companies continue to shift toward AI and machine learning (ML) processes, and business leaders are quickly realizing the potential gains from investing in them. These gains include faster, smarter automation, predictive analytics, and new-and-improved ways to establish …
In this edition of our roundup, news on: AI in Retail, Interactive Vending Machines, and Voice Recognition.
In this edition of our roundup, news on: London Metropolitan Police, MIT Drug Research, and AI Art at Auction.
According to industry research firm TechEmergence, Artificial Intelligence (AI) technology will have the single most radical, transformational impact on business and society. AI is driving the fourth industrial revolution or 4IR—the newest industrial era, which is characterized by emerging technological disruption and increasingly blurred lines between the physical and digital worlds. Organizations are rapidly exploring the business gains that can …
In this inaugural edition of our roundup, news on: robotic hands, Google Glass, and AI in China.
Several weeks ago, we shared a detailed report from the McKinsey Global Institute that analyzes hundreds of use cases for artificial intelligence (AI), and provides a perspective on how firms across a variety of industries can most benefit from this exciting technology. One area featured in the report is AI in the retail industry. According to the research highlights, use …
The Internet of Things (IoT) is a prime example that highlights why the quality and quantity of data are both critical for successful machine learning (ML) and artificial intelligence (AI) initiatives.
When implementing any strategic initiative, it’s important for organizations to build a considered plan upfront taking in a number of variables. This principle certainly holds true in the fast emerging areas of artificial intelligence (AI) and machine learning (ML).
With all the hype around AI, how can organizations determine where best to apply the technology?
The unstoppable march of Artificial Intelligence (AI) and machine learning is already touching our lives in so many ways. But its effects have only just begun to take hold.
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