#TinyML in Data Science

[tpreetitravling]

TinyML: Shaping the Future of Intelligent Edge Devices" explores the revolutionary potential of Tiny Machine Learning (TinyML) in enhancing edge computing. It elucidates how TinyML empowers resource-constrained edge devices with machine learning capabilities, enabling real-time decision-making and enhancing efficiency. The summary delves into the significance of TinyML in addressing privacy concerns, offering energy-efficient solutions, and revolutionizing various sectors like healthcare, agriculture, and industrial IoT. It also outlines key development processes and educational opportunities in TinyML. Overall, the title captures the essence of how TinyML is reshaping the future of edge devices, paving the way for a more intelligent and connected world.

[anusha-g]

Latest developments in data science

Transformers in Data Science:

Continued advancements in the use of transformer architectures for various tasks, especially in natural language processing (NLP) and computer vision.

Meta-Learning:

Growing interest in meta-learning, where models are trained to adapt and learn quickly from new tasks with limited data.

AI Ethics and Fairness:

Increasing focus on ethical considerations in AI, addressing issues of bias, fairness, and responsible AI practices.

Automated Machine Learning (AutoML) Platforms:

Continued evolution of AutoML tools and platforms, simplifying the machine learning workflow and making it more accessible.

Explainable AI (XAI):

Ongoing research and development in explainable AI techniques to improve the interpretability of complex machine learning models.

Generative Adversarial Networks (GANs):

Advancements in GANs for tasks such as image synthesis, style transfer, and data augmentation.

Edge AI and TinyML:

Growing applications of AI at the edge, including TinyML, which involves deploying machine learning models on resource-constrained devices.

Privacy-Preserving Machine Learning:

Continued efforts in developing techniques for privacy-preserving machine learning, ensuring the protection of sensitive data.

Reinforcement Learning Applications:

Expanding applications of reinforcement learning in robotics, gaming, and optimization problems, with a focus on improving stability and efficiency.

Natural Language Processing (NLP) Innovations:

Ongoing innovations in NLP, including pre-trained language models and advancements in understanding context and semantics.

Graph Neural Networks (GNNs) in Real-World Applications:

Increasing adoption of Graph Neural Networks for real-world applications, such as social network analysis, fraud detection, and recommendation systems.

Quantum Machine Learning:

Exploration of the intersection between quantum computing and machine learning, with research focused on solving complex problems more efficiently.

Remember, these are dynamic trends, and the field of data science continues to evolve. Stay tuned to reputable sources, research publications, and industry discussions for the latest updates in data science.

[industrydesignservices]

Machine Learning’s Role in Business Trends for 2023

Machine learning (ML) and artificial intelligence (AI) are vital technologies that have the potential to transform our way of life completely. The potential of artificial intelligence to aid in developing humanity as a species is still being assessed, given how AI and machine learning are being applied in fields like health and space exploration. 2023 will be a turning point for introducing new trends and current advancements in these two technologies.

What makes machine learning crucial?

The use of machine learning based on data science has improved our lives. Machine learning can accomplish jobs more quickly than humans if properly trained. Organizations must first understand the potential and recent advancements of machine learning technology to map the most effective business processes. Staying current is also vital if you want to compete in the market. Organizations must first understand the potential and recent breakthroughs of machine learning technology to chart a course to the most effective business practices.

Top 7 Latest Trends in Machine Learning

Below are some top trends in machine learning:

No-Code Machine Learning

Even if most machine learning is managed and set up by computer code, this is only occasionally the case. One may create machine learning applications without going through the drawn-out and time-consuming processes of preprocessing, modeling, designing algorithms, acquiring new data, retraining, deploying, etc. Some of the most important benefits of machine learning include the following:

Implementation is Speedy: Because there is no need to create code or troubleshoot, it will usually focus on results rather than development.

Reduced Costs: Large data science teams are no longer necessary since automation cuts down on extra development time.

Simpleness: No-code ML's drag-and-drop architecture makes it simple to use. Being an expert is unnecessary because the machine learning process has been greatly simplified. Machine learning is a fantastic alternative for analyzing data and making predictions over time. Despite its drawbacks, no-code machine learning is a suitable choice for smaller companies without the budget for a data scientist team.

Tiny ML

In a world where IoT technologies are increasingly taking over, TinyML enters the battle. Although there are several large-scale machine-learning applications, they are rarely used. A machine learning algorithm will process data after a web request has taken a long time to reach a big server. Instead, we can decrease latency and power use by using IoT edge devices to execute smaller-scale ML algorithms.

Industry hubs, the healthcare sector, the agricultural industry, and other businesses may profit from this cutting-edge technology. Since the data is not transported to a data processing center, there is a significant reduction in latency, bandwidth, and power usage.

Auto ML

AutoML strives to offer a straightforward and approachable solution without needing ML knowledge. Data scientists working on machine learning projects must do several activities, including preprocessing data, identifying features, modeling, building neural networks if deep learning is employed in the project, post-processing, and outcome analysis.

Due to human error, outside labor typically performs data labeling. The amount of the labeling process that AutoML automates considerably lowers the likelihood of human error. Additionally, organizations can focus more on data analysis by reducing human costs. Data analysis, artificial intelligence, and other technologies will be more accessible and inexpensive to enterprises because AutoML reduces these costs.

For instance, AutoWEKA allows users to select a machine learning method and its hyperparameters simultaneously; when used in conjunction with the WEKA package, it automatically creates powerful models for various data sets. A Python module called Auto-sklearn extends AutoWEKA and may replace the standard classifiers and regressors in sci-kit-learn.

Management of ML Operationalization

A machine learning program emphasizing reliability and effectiveness is called MLOps (Machine Learning Operationalization Management). This is a creative way to improve the creation of machine-learning solutions and make them more beneficial to businesses. The development and deployment of AI systems are combined into a single process by MLOps, a new methodology.

Understanding the importance of MLOps requires understanding the lifespan of machine learning systems.

Make a model based on the goals of your business.

For the ML model, data has to be gathered, cleaned up, and prepared.

Develop and improve your machine learning model.

Verify the computer learning model

Deploy an integrated model-containing software solution.

Monitor and resume the procedure to enhance the ML model.

They can address concerns such as fluctuating goals and a lack of internal communication across teams. For scalable enterprises, MLops solutions also minimize unpredictability and offer consistency and reliability. Instead of attempting to fix every issue simultaneously, we can better gather data and incorporate ML solutions by employing a business objective-first design.

For instance, Kubernetes's DevOps platform effectively allots hardware resources for AI/ML workloads, including RAM, CPU, GPU, and storage. Kubernetes offers real-time resource optimization for computing resources and enables auto-scaling.

Full-stack Deep Learning

There is a high demand for "full-stack deep learning" due to the widespread usage of deep learning frameworks and the need for businesses to integrate deep learning solutions into their products.

How does full-stack deep learning function? What is it? Consider that you have a group of expert deep learning engineers that have already created a stunning deep learning model for you. However, only a few files are disconnected from the environment where your users live while the deep learning model is constructed.

The next stage for engineers is to embed the deep learning model in some infrastructure:

Certain cutting-edge devices exist (Raspberry Pi, NVIDIA Jetson Nano, etc.).

A cloud-based mobile app backend

The demand for full-stack deep learning results in creation of libraries, frameworks, and educational programs that help engineers quickly adapt to shifting business demands (like open-source full-stack deep learning projects). Libraries and frameworks help engineers automate various shipping processes.

Generative Adversarial Networks

GANs can create more reliable solutions for problems like differentiating between various photo kinds. Discriminative neural networks must verify samples produced by general neural networks by filtering out any unnecessary generated data.

For anime and cartoons, GAN could automatically produce facial images. The generative adversarial network is trained using a particular dataset, such as illustrations of anime characters. New characters are created by the GAN by analyzing the provided set of pictures.

Reinforced Learning

In reinforcement learning, the computer learning system gains knowledge by directly interacting with its environment. The environment can use a reward/punishment mechanism to provide value to the observations that the ML system notices. The algorithm will eventually aim for the highest level of reward or value, much like positive reinforcement training for animals.

In reinforcement learning, an algorithm makes decisions based on irrational behavior and may purposely take unnecessary risks. This holds great promise for AI in video and board games, but it might endanger humans if left unchecked. Systems for safer reinforcement learning are now being developed that consider safety.

Reinforcement learning will become a much more potent tool in a data scientist's arsenal once it can complete tasks in the actual world without taking risks or doing harm. Tasks involving autonomous driving, such as motion planning, dynamic pathing, controller optimization, and scenario-based learning for highways, may use reinforcement learning.

For instance, understanding automated parking regulations can help with parking. A crash-free overtaking technique may be learned while maintaining a steady speed, and Q-Learning can be utilized to change lanes.

Conclusion

We must innovate to accomplish our aims in novel and distinctive ways if we are to enter previously unimaginable places. Each goal requires a different approach to be realized. Your business can increase productivity and achieve its aim of assisting clients with the aid of machine learning and data science. This has occasionally made it necessary for technology to stay competitive.

Discover the transformative power of Machine Learning in shaping business trends for 2023 through our latest blog.

[angrygiveralpaca]

Top 5 machine learning trends to know in 2023

Below is information on the computing device getting-to-know traits that emerged in 2022. Let’s get started.

Machine learning knowledge creates algorithms that help machines in higher comprehend information and make data-driven judgments. To take full gain of desktop studying trends, IT and enterprise leaders will want to boost a method for aligning AI with worker pursuits and commercial enterprise goals. According to predictive analyses, computers getting to know will emerge as pretty big through 2024. Below is information on the desktop studying tendencies that emerged in 2022. Let’s get started. Machine Learning Operationalization Management: Machine Learning Operationalization Management or MLOps's important reason is to streamline the improvement procedure of computing devices gaining knowledge of solutions. MLOps additionally helps deal with challenges that occur in jogging your business, like crew communication, the building of appropriate ML pipelines, and administration of touchy information at scale. Reinforcement Learning: The computing device gaining knowledge of the device learns from experiences with its environment in reinforcement learning. This has a lot of practicable in AI for video video games and board games. However, the place utility security is a priority, reinforcement ML might also no longer be the best option. 

Quantum ML: Quantum computing indicates awesome promise for developing extra effective AI and computer mastering models. The technological know-how is nevertheless past sensible reach, however, matters are beginning to trade with Microsoft, Amazon, and IBM making quantum computing sources and simulators effortlessly on hand by using cloud models. 

General adversarial networks: GANs, or General Adversarial Networks, are new ML developments that produce samples that ought to be reviewed by way of networks that are selective in nature and can delete any kind of undesired content. ML is the wave of the future, and each and every business enterprise is adjusting to this new science.

 

No-Code Machine Learning: Machine mastering is an approach for growing ML purposes except going via the prolonged and time-consuming strategies of preprocessing, modelling, constructing algorithms, retraining, deployment, etc.

 Automated laptop learning: Automated ML will have extended equipment for labelling facts and the automated tuning of neural internet architectures. The want for labelled facts had created a labelling enterprise of human annotators primarily based in less expensive countries. By automating the work of choosing and AI will emerge as more cost-effective and new options will take much less time to attain the market.

 Internet of Things: IoT will have a huge effect on 5G adoption as it will come to be the basis for IoT. Systems will be in a position to get hold of and ship records at a quicker charge due to the fact of 5G’s tremendous community speed. Other machines on the gadget can be related to the net by way of IoT devices.

 Improved cybersecurity: With the development of technology, most apps and units have emerged as smart, ensuing in enormous technological advancement. Tech professionals may additionally make use of desktop mastering to create anti-virus fashions that will block any viable cyber-attacks and decrease dangers.

 TinyML: TinyML is a higher approach as it lets in the quicker processing of algorithms when you consider that facts don’t have to journey again and forth from the server. This is specifically necessary for larger servers, thereby making the complete system much less time-consuming. 

Multi-modal learning: AI is getting higher at assisting a couple of modalities inside a single laptop gaining knowledge of models, such as text, vision, speech, and IoT sensor data. Developers are beginning to discover modern methods to mix modalities to enhance frequent duties like record understanding.

[damiencordle]

I Found This Interesting. Joshua Damien Cordle

AI models can now continually learn from new data on intelligent edge devices like smartphones and sensors

Microcontrollers, miniature computers that can run simple commands, are the basis for billions of connected devices, from internet-of-things (IoT) devices to sensors in automobiles. But cheap, low-power microcontrollers have extremely limited memory and no operating system, making it challenging to train artificial intelligence models on "edge devices" that work independently from central computing resources.

Training a machine-learning model on an intelligent edge device allows it to adapt to new data and make better predictions. For instance, training a model on a smart keyboard could enable the keyboard to continually learn from the user's writing. However, the training process requires so much memory that it is typically done using powerful computers at a data center, before the model is deployed on a device. This is more costly and raises privacy issues since user data must be sent to a central server.

To address this problem, researchers at MIT and the MIT-IBM Watson AI Lab developed a new technique that enables on-device training using less than a quarter of a megabyte of memory. Other training solutions designed for connected devices can use more than 500 megabytes of memory, greatly exceeding the 256-kilobyte capacity of most microcontrollers (there are 1,024 kilobytes in one megabyte).

The intelligent algorithms and framework the researchers developed reduce the amount of computation required to train a model, which makes the process faster and more memory efficient. Their technique can be used to train a machine-learning model on a microcontroller in a matter of minutes.

This technique also preserves privacy by keeping data on the device, which could be especially beneficial when data are sensitive, such as in medical applications. It also could enable customization of a model based on the needs of users. Moreover, the framework preserves or improves the accuracy of the model when compared to other training approaches.

"Our study enables IoT devices to not only perform inference but also continuously update the AI models to newly collected data, paving the way for lifelong on-device learning. The low resource utilization makes deep learning more accessible and can have a broader reach, especially for low-power edge devices," says Song Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, and senior author of the paper describing this innovation.

Joining Han on the paper are co-lead authors and EECS PhD students Ji Lin and Ligeng Zhu, as well as MIT postdocs Wei-Ming Chen and Wei-Chen Wang, and Chuang Gan, a principal research staff member at the MIT-IBM Watson AI Lab. The research will be presented at the Conference on Neural Information Processing Systems.

Han and his team previously addressed the memory and computational bottlenecks that exist when trying to run machine-learning models on tiny edge devices, as part of their TinyML initiative.

Lightweight training

A common type of machine-learning model is known as a neural network. Loosely based on the human brain, these models contain layers of interconnected nodes, or neurons, that process data to complete a task, such as recognizing people in photos. The model must be trained first, which involves showing it millions of examples so it can learn the task. As it learns, the model increases or decreases the strength of the connections between neurons, which are known as weights.

The model may undergo hundreds of updates as it learns, and the intermediate activations must be stored during each round. In a neural network, activation is the middle layer's intermediate results. Because there may be millions of weights and activations, training a model requires much more memory than running a pre-trained model, Han explains.

Han and his collaborators employed two algorithmic solutions to make the training process more efficient and less memory-intensive. The first, known as sparse update, uses an algorithm that identifies the most important weights to update at each round of training. The algorithm starts freezing the weights one at a time until it sees the accuracy dip to a set threshold, then it stops. The remaining weights are updated, while the activations corresponding to the frozen weights don't need to be stored in memory.

"Updating the whole model is very expensive because there are a lot of activations, so people tend to update only the last layer, but as you can imagine, this hurts the accuracy. For our method, we selectively update those important weights and make sure the accuracy is fully preserved," Han says.

Their second solution involves quantized training and simplifying the weights, which are typically 32 bits. An algorithm rounds the weights so they are only eight bits, through a process known as quantization, which cuts the amount of memory for both training and inference. Inference is the process of applying a model to a dataset and generating a prediction. Then the algorithm applies a technique called quantization-aware scaling (QAS), which acts like a multiplier to adjust the ratio between weight and gradient, to avoid any drop in accuracy that may come from quantized training.

The researchers developed a system, called a tiny training engine, that can run these algorithmic innovations on a simple microcontroller that lacks an operating system. This system changes the order of steps in the training process so more work is completed in the compilation stage, before the model is deployed on the edge device.

"We push a lot of the computation, such as auto-differentiation and graph optimization, to compile time. We also aggressively prune the redundant operators to support sparse updates. Once at runtime, we have much less workload to do on the device," Han explains.

A successful speedup

Their optimization only required 157 kilobytes of memory to train a machine-learning model on a microcontroller, whereas other techniques designed for lightweight training would still need between 300 and 600 megabytes.

They tested their framework by training a computer vision model to detect people in images. After only 10 minutes of training, it learned to complete the task successfully. Their method was able to train a model more than 20 times faster than other approaches.

Now that they have demonstrated the success of these techniques for computer vision models, the researchers want to apply them to language models and different types of data, such as time-series data. At the same time, they want to use what they've learned to shrink the size of larger models without sacrificing accuracy, which could help reduce the carbon footprint of training large-scale machine-learning models.

This work is funded by the National Science Foundation, the MIT-IBM Watson AI Lab, the MIT AI Hardware Program, Amazon, Intel, Qualcomm, Ford Motor Company, and Google.

Story Source:

Materials provided by Massachusetts Institute of Technology. Original written by Adam Zewe. Note: Content may be edited for style and length.

Journal Reference:

Ji Lin, Ligeng Zhu, Wei-Ming Chen, Wei-Chen Wang, Chuang Gan, Song Han. On-Device Training Under 256KB Memory. Submitted to arXiv, 2022 [abstract]

[360digitmg-bangalore]

The 5 Biggest Data Science Patterns In 2022

Distinguishing the valuable data sources and computerizing the course of variety. It might likewise supply an extraordinary option for the worldwide clinical specialists in sharing their crucial data about and helping sickly patients from any niche of the world. Ventures like FMCG, Online business, Style, Money, Clinical and even state run administrations are tapping this large amount of information for different targets. As the world has guided inside the Computerized time frame inside the last part of the 90s, no one would have figured we could come this far in basically 2 quite a while. The simple to the computerized world excursion has been downright a marvel. What's more, at the focal point of this fantastic transformation, there's one drive that is driving the entire framework, Information. It will bring about momentous improvements in clinical sciences soon. At last, it will prompt the formation of additional positions in the field of Information Science in 2021. Information Science is most likely surely one of the most smoking and moving applied sciences across the globe. It is enlarging its areas of uses in associations, from top MNCs to new businesses. Thus, in this weblog, we'll investigate the improvements in Information Science in 2021 and its extension exhaustively. Analysts are the people working with hypothetical or applied insights. TinyML alludes to machine concentrating on calculations intended to take up as little region as feasible to permit them to run on low-fueled equipment, close to the spot the activity is. Having an incredible hold of computational abilities might assist you with standing apart from the pack. Knowing polynomial math and analytics is an additional benefit as it is used in ML and profound research. Having a decent information of measurements and chance is one more advantage as they are many times supportive in examining and envisioning information to determine experiences. As the spread of Coronavirus spread all through India, a great deal of the cycles began occurring on-line. The conjunction of immense information flood and the need to outfit this huge measure of data has together built a difficult task market in information science. Named as "one of the hottest positions in 2021" by the Harvard Business Survey, information science has presented a total new exhibit of occupations to the market. Particularly in an emerging country like India, there may be degree for enormous information related tasks comparing to information researchers, data examination, huge information engineers, large data chiefs, and information designers. Information Science is essential in medical services to keep screen of the patients' wellbeing and help specialists to grasp the examples of disease and to forestall it.

data science in hyderabad

  In this text, we will cowl a top to bottom comprehension of the data researchers work market in India and how the need is inclined to develop sometime. As the associations are progressively understanding the genuine capability of data researchers, occupations on this space are expected to soar. In spite of the log jam in the market of occupations because of Coronavirus, the work market of information science stays to be productive. Subsequently, it's a decent choice for up-and-comers with an interest in big business organization and assessment. Moreover, AutoML can introduce accommodating shields for the adequacy of logical results, for example, sorting out information spillage from preparing information that isn't line wise autonomous and indistinguishably dispersed. Mechanizing highlight determination and hyperparameter search via robotized hyperparameter tuning can furthermore be going to be a welcome life hack for information researchers and non-specialists the same. As associations are turning towards ML, colossal information, and artificial intelligence, the market for information researchers is supporting. Information science has simplified typical lives by observing things near one's home or office, improving the norm of web based shopping, empowering safeguarded on-line reserve exchanges, and loads of extra. This is a legend on the grounds that DevOps board almost works with the engineers for dealing with the pattern of utilizations. The word 'information science' was begat in 2008 when ventures comprehended the necessity for information experts who are knowledgeable about dissecting and putting together a colossal extent of information. Gain Information Science Course in Hyderabad from Master Workforce to start out your calling as data researcher. Information Science experts with the right abilities and wanted skill can make genuine colossal money. Generative man-made intelligence has quickly transform into implanted inside human expression, and relaxation business, where we presently have seen Martin Scorsese de-age Robert DeNiro in The Irishman and a youthful Imprint Hamill appear to be in The Mandalorian. Procure yourself a promising profession in information science by signing up for the Experts in Information Science Program provided by 360DigiTMG. As a fresher or someone just starting out in this subject, endeavor to get a data science temporary job, even whether it is neglected. Bundling of the Information is where the models are made, the insights are used, and the representation is created. Having information is great yet having an endorsements in a similar space acts free to your range of abilities. Indeed, even freshers can get great job offers if they feature a decent group of work along with awell-made portfolio. This highlights an elegantly composed continue containing every important capacity and abilities and specifics of past undertakings if any. This guarantees that the best positions in this day and age are Information Researcher, Information Expert, Information Architect, and Business Examiner. As indicated by an IBM report, Information Science occupations would almost certainly foster by 30%. The pandemic has pulled together consideration on cutting edge examination as a vigorous gadget to deal with vulnerability and answer to rapidly moving key circumstances. The ascent in AIaaS will add to the universality of artificial intelligence driven choices, which is in a situation to now end up being doable for a lot more extensive scope of business sectors. Likewise, AutoML and expanded investigation is consistently diverting information science from a set of working responsibilities to a task expertise — one which may be vital for directors looking for improvement. Furthermore, 2022 could be the year when the frequently refered to maxim about information being the New Oil would draw nearer to reality with the first 'ware trades' for data. While AutoML instruments have been around for quite a long time, 2022 is probably going to put new accentuation on such answers for mitigate the ceaseless shortage of data researchers. A most recent report by McKinsey featured that cash spent on chasing after scant information science ability might be more significant in selecting and preparing skilled, capable clients of AutoML. Huge designs prepared on large number of circumstances like GPT-3 or DALL-E might get the titles, yet TinyML is on the ascent. Basically, TinyML is the hotly anticipated combination of inserted strategies with AI. The IoT worldview has to a great extent depended on crude data from edge gadgets, from smartwatches to electrical energy meters, being carried to enormous run of the mill servers that may then execute progressed machine concentrating on calculations. Be that as it may, over the past two or three years, the expense of handling energy has in practically no time diminished, though the expense of information switch has remained generally something similar. So the researchers need to orchestrate the information for additional examining i.e., rebuilding and planning the information from crude to cooked structure. Luckily, the TensorFlow manufacturers have gotten on the capability of TinyML, and made TensorFlow Light, which can pack and advance designs to run on 8-cycle whole numbers. As edge units transform into pervasive, from "shrewd" kitchen machines to live peculiarity location and observing of automated modern offices, the TinyML worldview will gain floor. Information researchers may at first find the expectation to learn and adapt of equipment and low-level programming overwhelming, yet the developing multiplication of TinyML systems makes certain to offer this space a significant elevate in 2022. Photograph by Jorge Ramirez on UnsplashAt the guts of TinyML is a rising believe that for machine concentrating on developers should know their basic equipment. Where sources are successfully boundless, there could be minimal must funds computational power. On the possibility of investigation by Glassdoor, inside the US alone, information researchers are getting bundles cost $116,100 p.a.

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Artificial Intelligence (AI): What’s In Store For 2021?

New Post has been published on https://perfectirishgifts.com/artificial-intelligence-ai-whats-in-store-for-2021/

Artificial Intelligence (AI): What’s In Store For 2021?

Side face of AI robot network with data.

This was a banner week for AI (Artificial Intelligence). The reason? Well, C3.ai came public and soared on its debut. It’s certainly a validation of the importance of enterprise AI. Keep in mind that C3.ai provides comprehensive software solutions and services for a myriad of large companies, including 3M, Royal Dutch Shell, Raytheon, Baker Hughes and conEdison.

“The use of AI and data analytics will become increasingly important in IT as organizations aim to deliver seamless support and predictive capabilities,” said Amit Sawhney, who is the Vice President of Services Operations at Dell Technologies.

So then, given all the investment and innovation, what might we see next year with AI? As should be no surprise, there is quite a bit. So let’s take a look:

Sri Viswanath, the Chief Technology Officer of Atlassian:

“In the next 5 years, increased data and privacy regulation will have a big impact on the way we design AI/ML models. As a result, investment in data management is going to be critical in determining the success of AI systems. Companies that have better data management frameworks, platforms and systems will win in building effective AI tools.”

Anand Rao, the Global Artificial Intelligence Lead at PwC:

“Our latest AI research shows 86% of businesses currently reaping the benefits of better customer experience through AI, and 25% of companies with widespread AI adoption expect to see the tech pay out in increased revenue during 2021.  The pandemic has uncovered the value of AI, lending itself to enhancing tasks related to workforce planning, simulation modeling and demand projection.”

Rohan Amin, the Chief Information Officer at Chase:

“In 2021, we will see more sophisticated applications of artificial intelligence and machine learning (AI/ML) across industries, including financial services. There will be greater integration of AI/ML models and capabilities into multiple business processes and operations to drive improved insights and better serve customers.”

Kimberly Nevala, the AI Strategic Advisor at SAS:

“AI adoption will continue to gain traction in 2021 with emphasis on decisions that are not at the mercy of seismic shifts resulting from the ongoing pandemic. The focus will remain on applying AI to automating and augmenting core business processes where the problem space is relatively stable and desired outcomes are well-bounded. While this may seem reactionary, this continues a 2020 trend in which AI adopters at all levels reported that enhancing existing products and services was their number one AI priority.”

Wilson Pang, the Chief Technology Officer at Appen:

“In 2021, we’ll see organizations moving past just acknowledging and ‘worrying’ about bias in AI and start to make more significant moves to solve for it–because it will be required. Specific teams and/or initiatives will be formed to combat all the concerns that fall under the umbrella of responsible AI, including everything from inherent bias in data to treating data trainers fairly.”

Michael Berthold, the CEO and cofounder of KNIME:

“Because cloud and hybrid environments will become much more prevalent, data science will have to adapt. It will need to be conducted in a variety of environments and shared across them in order to maximize effectiveness.”

Steve Grobman, the Chief Technology Officer of McAfee:

“Advances in AI technologies, including generative adversarial networks, will make disinformation through fake content, such as deepfake videos and auto-generated social media posts, virtually indistinguishable from real content.” 

Ram Chakravarti, the Chief Technology Officer at BMC:

“In 2021 we will see the impacts of AI on today’s enterprise via pervasive intelligence. This will have significant effects on how companies approach enterprise automation as well as their basis for the growth strategy.”

Peter Reinhardt, the CEO and cofounder of Segment:

“Consumer companies, which tend to have more traffic and data than B2B businesses, will see the most (and quickest) improvement in their AI/ML applications, if they test with customers and iterate. While these use cases (e.g. content ranking) may not seem futuristic, they will drive meaningful business impact.”

Bill Scudder, the General Manager of AIoT Solutions at AspenTech:

“In 2021, we’ll see more industrial organizations increase investment in lowering the barriers to AI adoption by deploying targeted embedded Industrial AI applications that combine data science and AI with purpose-built software and domain expertise. This will be the key to overcome a lack of skills and drastically reduce the need for many data scientists.”

Scott Prevost, the Vice President of Engineering at Adobe Sensei:

“The most powerful application of AI will be the complement of human EQ with machine IQ–where human ingenuity will merge with the power of machines to enhance human creativity and intelligence (not replace it). AI is evolving from being another technology in one’s arsenal to a virtual ‘co-pilot’ that can help businesses achieve their goals faster and streamline consumer workflows.”

Clemens Mewald, the Director of Product Management of Machine Learning and Data Science at Databricks:

“We’ll see enterprise customers moving away from building their own machine learning platforms, recognizing that it’s not their core competency. They’ll realize that more value comes from applying ML to business problems versus spending the time to build and maintain the tools themselves.” 

Bob Friday, the Chief Technology Officer at Mist Systems, a Juniper Networks company:

“Cloud and AI will turn the customer support between both the enterprise and their customers / employees and between the enterprise and their infrastructure vendor upside down. With cloud AI, the vendor will let the enterprise customer know when there is a hardware or software problem. The days of arguing with their vendors on hardware and software problems are over.”

Michael Beckley, the Chief Technology Officer and cofounder of Appian:

“Software vendors and AI providers such as Google and AWS will continue to strip the complexities out of operationalizing AI by using low-code techniques. In 2021, the use of broadly-applicable and high-value use cases like AI-enabled Document Processing will become widespread.”

Jason Tan, the CEO of Sift:

“In 2021 we will see a marked increase in the number of lawsuits filed implicating artificial intelligence technologies. While we’ve seen high-profile suits brought against companies over the last few years, AI is simply more prevalent in our everyday lives. As an immature technology, we’re going to see AI systems make more (and new) mistakes that carry real human impact. When mistakes are made, consumers will take legal action.”

Tim Tully, the Chief Technology Officer at Splunk:

“More and more is happening at the edge, because we can do more and more computation as the hardware and software gets more sophisticated. Local processing reduces the latency of moving the data to the cloud to process, and you get the same results.”

Christine Boles, the Vice President of the IoT Group and General Manager of the Industrial Solutions Division at Intel:

“The pandemic has greatly accelerated the need for companies to complete their Industry 4.0 transformations with solutions that allow them to have more flexibility, visibility and efficiency in their operations. We’ll see an acceleration of adoption of solutions that help address that need, ranging from AI including machine learning, machine vision and advanced analytics.”

Dr. Rana el Kaliouby, the cofounder and CEO of Affectiva:

“Emotion AI software that can understand nuanced human emotions and complex cognitive states based on facial and vocal expression will address some of technology’s shortcomings in light of the pandemic, and we’ll see companies using it for new use cases.”

Rick Rider, who is the Vice President of Product Management at Infor:

“In the unpredictable job market of 2021, it will be critical for organizations to leverage AI to ensure they find the right candidate for the job. AI will enable HR departments to become more proactive in their hiring and help them determine a candidate’s cultural fit by using data to measure the quality of a hire.”

Richard Tomlison, the Senior Director of Product Marketing at DataRobot:

“In 2021 we expect budgets to be consolidated and organizations will be looking to minimize the number of AI software vendors they deal with. The market has moved from point solutions and towards full solutions with end-to-end value. It is no longer acceptable or even feasible to have multiple disparate products solving multiple disparate problems.”

Flavio Bonomi, the Board Advisor to Lynx Software Technologies:

“2021 is the year where AI will get embedded into existing devices and make certain functionality faster and more accurate as standard. Sensors can now detect any of the five senses (yes, including smell) and we will see AI increasingly applied to all of those. Examples include the ability to detect vibrations or unusual noises in a factory that ensures maintenance is performed on equipment prior to it malfunctioning. Not as sexy or as obvious as a self-driving vehicle, but practical and with a measurable ROI.”

Jason Shepherd, the Vice President of Ecosystems at ZEDEDA:

“The TinyML conversation that started heating up in late 2019 will reach full-on buzz in 2021. This means more on-device processing, including in smart cameras, and further realization by the telcos and traditional IT players that not all edge processing will happen in a data center.”

Jan Gilg, the President of SAP S/4HANA:

“In 2021, we will continue to see companies leverage data and intelligent technologies to realize smart, data-driven insights that they have never had access to before without a large-scale implementation.”

Dan Simion, the Vice President of AI and Analytics at Capgemini North America:

“In 2021, we will see an evolution of AI solutions to solve technical problems automatically and without human intervention. This self-healing mechanism will self-correct malfunctions proactively to keep critical applications operational and reduce the risk of systems shutting down.”

Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction and The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems. He also has developed various online courses, such as for the COBOL and Python programming languages.

From Entrepreneurs in Perfectirishgifts