Smart devices keep getting smarter and are demanding more and more out of the hardware. How can we make sure that these devices are compatible with the artificial intelligence needed to keep them functioning, without having to increase the hardware capacity? This is what Nesma Rezk, Ph.D. in Computer Science and Engineering, has been researching in her dissertation. Nesma Rezk's thesis is about implementing deep learning applications on embedded platforms, which is any type of computer system with a dedicated function, such as a smart watch or an autonomous car. Deep learning is a type of artificial intelligence (AI) technique that teaches computers to learn by example.

It's never been easier for businesses of all sizes to harness the power of data, thanks to the development of free, open-source machine learning algorithms and artificial intelligence tools like Google's TensorFlow and scikit-learn, as well as "ML-as-a-service" products like Google's cloud prediction API and Microsoft's Azure machine learning platform. On the other hand, machine learning is a significant and complicated field. Where do you begin to learn how to apply it to your company? Machine learning is a branch of study that trains machines to do cognitive tasks like humans do. While they have far fewer cognitive abilities than ordinary people, they can quickly process large amounts of data and extract significant commercial insights.

Deep learning applications for natural language processing have given AI the ability to emulate human perception and cognition and has moved the technology one step closer to perfectly replicating the human abilities. The use of deep learning or deep neural networks has enabled scientists to create AI that can process and understand the normal human language. This capability, termed as natural language processing, has enabled AI applications to collect, recognize, and classify unstructured data, which had been a challenge until recently. Deep learning applications for natural language processing can help AI read input from various unstructured sources like images, videos, and text. Deep learning can enable AI systems to make sense of unstructured data, such as text from different sources.

People can acquire and apply general knowledge to solve problems in a wide range of subject areas. Some of them are absorbed by all people, such as walking and talking. Over the past five years, AI has made a huge impact. Not a day goes by without media coverage of AI applications, and start-up activity skyrocketing and new ventures thriving in the field. For example, according to this report, AI companies increased their activity by 72% in 2018 compared to 2017.

How often have you heard "The Machine Learning Application worked well in the lab, but it failed in the field. It is not the fault of the Machine Learning Model! This blog is not yet another blog article (YABA) on DataOps, DevOps, MLOps, or CloudOps. I do not mean to imply xOps is not essential. For example, MLOps is both strategic and tactical. It promises to transform the "ad-hoc" delivery of Machine Learning applications into software engineering best practices. We know the symptoms: Most machine-learning models trained in the lab perform poorly on real-world data [1, 2, 3, 4]. Machine Learning created profits in the year 2020 and will continue to increase profits in the future. However, many problems hold back the progress and success of Machine Learning application rollout to production. I focus on what it is the most significant problem or cause: the quality and quantity of input data in Machine Learning models [1,4]. We realized the quantity of high-quality data was the bottleneck in predictive accuracy when we started showing near, or above, human-level performance in structured data, imagery, game playing, and natural language tasks. How many times do we look at the Machine Learning application lifecycle's conceptualization to realize a Machine Learning model is not at the beginning (Figure 2)? We can research and improve the tools of the Machine Learning application lifecycle. But that only lowers the cost of deployment. Arguably, the Machine Learning model's choice is not a critical part of deploying a Machine Learning application. We have a "good enough" process or pipeline to choose and change the Machine Learning model, given a training input dataset. However, when achieving State-of-the-Art (SOTA) results, the input data seems to have the most significant impact on the output predictive data (Figure 2). We seem to know the cause: input data that was garbage results in garbage output predictive data. New data input to a trained Machine Learning model determines the accuracy of the output. We divide Machine Learning input data into four arbitrary categories, defined by the Machine Learning application output accuracy. GPT-3 is an example [6]. GPT-3 trained with an enormous amount of data [6]. GPT-3 is frozen in time as a transformer that you access through an API. Concept Drift is a change in what to predict. For example, the definition of "what is a spammer." We do not cover Concept Drift here. I do not think of it as a problem but rather as a change in the solution's scope. An example of Case 2: Data Drift, is that Case 1: "It works!, is a temporal phenomenon.

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.

Artificial intelligence (AI) is becoming more common in clinical practice. Increased computing power, greater volumes of data generated, and progress in machine learning promise new possibilities in clinical research and patient care. However these developments also raise certain ethical and legal questions. How will the role of doctors and patients change if AI is used in diagnostic procedures? Who is responsible for the consequences of AI-assisted processes in clinical contexts?

A trio of researchers from Chonnam National University, Nanjing University of Information Science and Technology and the Chinese Academy of Sciences has found that a deep learning convolutional neural network was able to accurately predict El Niño events up to 18 months in advance. In their paper published in the journal Nature, Yoo-Geun Ham, Jeong-Hwan Kim and Jing-Jia Luo, describe their deep learning application, how it was trained and how well it worked in predicting El Niño events. El Niño-Southern Oscillation events are periods during which water warms above normal temperatures in tropical parts of the Pacific. When that warm water moves east, it leads to more rainfall and other weather events, such as hurricanes, in the Americas, and less rain in Australia and Indonesia. Current models can accurately predict such events using data from water temperature gauges spread across the globe up to a year in advance.

A two-person team from the UNICEF's Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning "Hackathon" As part of efforts to develop the technology and innovation ecosystem to support development of Sierra Leone, UNICEF is collaborating with the Directorate of Science, Technology and Innovation (DSTI) in the Office of the President, on a knowledge exchange partnership, around innovative Machine Learning techniques which, it is hoped, will add value to Government's work around data for decision making in the country. A two-person team from the UNICEF's Office of Innovation in New York recently joined DSTI in Sierra Leone to collaborate on a Machine Learning "Hackathon" to work on data from the education sector in support of the Government's Free Quality School Education initiative. Officials from different Government Ministries, Departments and Agencies joined the team to enhance their knowledge of Machine Learning and advanced data analysis techniques, for use in their own areas of government. Shane O'Connor, Technology for Development Specialist at UNICEF Sierra Leone, stated that the opportunity afforded by this collaboration is huge. "With the President's establishment of the DSTI and with UNICEF's collaboration, there really is great potential for a step change in how Technology and Innovation can be leveraged to deliver for Sierra Leone," he said.