Category Archives: Machine Learning
Why GPT-3 Heralds a Democratic Revolution in Tech – Built In
GPT-3, a machine learning model from OpenAI, has taken the world by storm over the last couple of weeks. Natural language generation, a branch of computer science focused on creating texts from a batch of data, entered a golden age with last years release of GPT-2. The release of GPT-3 last month only confirmed this. In this article, I want to take a look at why GPT-3 is such a huge deal for the machine learning community, for entrepreneurs, and for anyone working with technology.
GPT-3 is a 175 billion parameters Transformer deep learning model. That might sound complicated, but it boils down to an algorithm that was taught to predict the next word based on the sentence you input. After you provide a sentence and the algorithm fills in the gaps. For example, you could put in How to successfully use content marketing? and you would get a text on the subject of content marketing.
GPT stands for Generative Pre-Training. The generative part of that term should be clear. You want the model to generate a text for you based on some input. Pre-Training refers to the fact that the model was trained on a massive corpus of text and its knowledge of language comes from the examples it has seen before. It doesnt copy fragments of texts verbatim, however. The process involves randomness due to the fact that the model tries to predict the next word based on what came before, and this prediction has a statistical component to it. All this also means that GPT-3 doesnt truly understand the language its processing; it cant make logical inferences like a human can, for instance.
GPT-3 doesnt feature a real breakthrough on the algorithmic side. Its more of the same as GPT-2, although it was trained with substantially more data and more computing power. OpenAI used a C4 (Common Crawl) dataset from Google, which Google used in training their T5 model.
So why is GPT-3 amazing at all? Its transformative nature all boils down to its applications, which is where we can really measure its robustness.
Imagine you want to build a model for translation from English to French. Youd take a pre-trained language model (say BERT) and then feed an English word or sentence into it as date along with a paired translation. GPT-3 can perform this task and many others without any additional learning, whereas youd need to fine-tune earlier machine learning models like BERT on each task. You simply provide a prompt (asking sentence or phrase):
Translate English to French: cheese =>to getfromage
Providing a command without additional training is what we call zero-shot learning. You gave no prior examples of what you wanted the algorithm to achieve, yet it understood that you wanted to make a translation. You could, for example, give Summarize as an input and provide a text that you wanted a synopsis of. GPT-3 will understand that you want a summary of the text without any additional fine-tuning or more data.
In general, GPT-3 is a few-shot learner, which means that you simply need to describe to it a couple of examples of what you want, and then it can figure out the rest. The most surprising applications of this include various human-to-machine interfaces, where you write in simple English and get a code in HTML, SQL, Python, or app design in Figma.
For example, this GPT-3 powered app lets you write How many users have signed up since the start of 2020? The app would then give you an SQL code: SELECT count(id) FROM users WHERE created_at > 2020-01-01 that does just that. In other words, GPT-3 allows you to make queries about spreadsheets using natural language English in this case.
Another great GPT-3 powered app lets you describe a design you want in simple English (Make a yellow Registration button) and get Figma files with the button ready to be implemented in your app or website.
There are plenty of other examples that feature GPT-3 translating from English to a coding language, making the interaction between humans and machines much easier and faster. And thats why GPT-3 is truly groundbreaking. It points us towards new, different human-machine interfaces.
So what does GPT-3 offer entrepreneurs, developers, and all the rest of us? Simplicity and the increasing democratization of technology.
GPT-3 and similar generative models wont replace developers or designers soon, but they will allow for wider access to technology, be that designing new apps, websites, or researching and writing about various topics. Non-technical people wont have to rely on developers to start playing around with their ideas or even build an MVP. They can simply describe it in English as they would to a software house to get what they want. This could well drive down the costs of entrepreneurship as you would no longer need developers to start.
What does that mean to developers, though? Will they become obsolete? Not at all. Instead,they will move higher up the stack. Their primary job is to communicate with the machine to make it do the things that the developer wants. With GPT-3 and similar generative models, that process will happen much faster. New programming languages emerge all the time for a reason: to make programming certain tasks easier and smoother. Generative language models can help build a new generation of programming languages which will power up developers to do incredible things much faster.
All in all, the impact of GPT-3 over the next five years is likely to be increasingly democratized technology. These tools will become cheaper and more accessible to anyone, just like the widespread access to the Internet did 20 years ago.
Regardless of the exact form it takes, with GPT-3, the future of technology definitely looksexciting.
P.S. If you want to test models similar to GPT-3 right now for yourself, visit Contentyze, a content generation platform Im building with my team.
Read the original here:
Why GPT-3 Heralds a Democratic Revolution in Tech - Built In
BMW, Red Hat, and Malong Share Insights on AI and Machine Learning During Transform 2020 – ENGINEERING.com
BMW, Red Hat, and Malong Share Insights on AI and Machine Learning During Transform 2020Denrie Caila Perez posted on August 07, 2020 | Executives from BMW, Red Hat and Malong discuss how AI is transforming manufacturing and retail.
(From left to right) Maribel Lopez of Lopez Research, Jered Floyd of Red Hat, Jimmy Nassif of BMW Group, and Matt Scott of Malong Technologies.
The VentureBeat 2020 conference welcomed the likes of BMW Groups Jimmy Nassif, Red Hats Jered Floyd, and Malong CEO Matt Scott, who shared their insights on challenges with AI in their respective industries. Nassif, who deals primarily with robotics, and Floyd, who works in retail, both agreed that edge computing and the Internet of Things (IoT) has become powerful in accelerating production while introducing new capabilities in operations. According to Nassif, BMWs car sales have already doubled over the past decade, with 2.5 million in 2019. With over 4,500 suppliers dealing 203,000 unique parts, logistics problems are bound to occur. In addition to that, approximately 99 percent of orders are unique, which means there are over 100 end-customer options.
Thanks to platforms such as NVIDIAs Isaac, Jetson AXG Xavier, and DGX, BMW was able to come up with five navigation and manipulation robots that transport and manage parts around its warehouses. Two of the robots have already been deployed to four facilities in Germany. Using computer vision techniques, the robots are able to successfully identify parts, as well as people and potential obstacles. According to BMW, the algorithms are also constantly being optimized using NVIDIAs Omniverse simulator, which BMW engineers can access anytime from any of their global facilities.
In contrast, Malong uses machine learning in a totally different playing fieldself-checkout stations in retail locations. Overhead cameras are able to feed images of products as they pass the scanning bed to algorithms capable of detecting mis-scans. This includes mishaps such as occluded barcodes, products left in shopping carts, dissimilar products, and even ticket switching, which is when a products barcode is literally switched with that of a cheaper product.
These algorithms also run on NVIDIA hardware and are trained with minimal supervision, allowing them to learn and identify products using various video feeds on their own. According to Scott, edge computing is particularly significant in this area due to the necessity of storing closed-circuit footage via the cloud. Not only that, but it enables easier scalability to thousands of stores in the long term.
Making an AI system scalable is very different from making it run, he explained. Thats sometimes a mirage that happens when people are starting to play with these technologies.
Floyd also stressed how significant open platforms are when playing with AI and edge computing technology. With open source, everyone can bring their best technologies forward. Everyone can come with the technologies they want to integrate and be able to immediately plug them into this enormous ecosystem of AI components and rapidly connect them to applications, he said.
Malong has been working with Open Data Hub, a platform that allows for end-to-end AI and is designed for engineers to conceptualize AI solutions without needing complicated and costly machine learning workflows. In fact, its the very foundation of Red Hats data science software development stack.
All three companies are looking forward to more innovation in applications and new technologies.
Visit VentureBeats website for more information on Transform 2020. You can also watch the Transform 2020 sessions on demand here.
For more news and stories, check out how a machine learning system detects manufacturing defects using photos here.
Algorithm created by deep learning finds potential therapeutic targets throughout the human genome – National Science Foundation
Researchers identified sites of methylation that could not be found with existing sequencing methods
Representation of a DNA molecule that is methylated. The two white spheres are methyl groups.
August 13, 2020
Researchers at the New Jersey Institute of Technology and the Children's Hospital of Philadelphia have developed an algorithm through machine learning that helps predict sites of DNA methylation -- a process that can change the activity of DNA without changing its overall structure. The algorithm can identify disease-causing mechanisms that would otherwise be missed by conventional screening methods.
DNA methylation is involved in many key cellular processes and is an important component in gene expression. Errors in methylation are linked with a variety of human diseases.
The computationally intensive research was accomplished on supercomputers supported by the U.S. National Science Foundation through the XSEDE project, which coordinates nationwide researcher access. The results were published in the journal Nature Machine Intelligence.
Genomic sequencing tools are unable to capture the effects of methylation because the individual genes still look the same.
"Previously, methods developed to identify methylation sites in the genome could only look at certain nucleotide lengths at a given time, so a large number of methylation sites were missed," said Hakon Hakonarson, director of the Center for Applied Genomics at Children's Hospital and a senior co-author of the study. "We needed a better way of identifying and predicting methylation sites with a tool that could identify these motifs throughout the genome that are potentially disease-causing."
Children's Hospital and its partners at the New Jersey Institute of Technology turned to deep learning. Zhi Wei, a computer scientist at NJIT and a senior co-author of the study, worked with Hakonarson and his team to develop a deep learning algorithm that could predict where sites of methylation are located, helping researchers determine possible effects on certain nearby genes.
"We are very pleased that NSF-supported artificial intelligence-focused computational capabilities contributed to advance this important research," said Amy Friedlander, acting director of NSF's Office of Advanced Cyberinfrastructure.
Originally posted here:
Algorithm created by deep learning finds potential therapeutic targets throughout the human genome - National Science Foundation
Ensighten Launches Client-Side Threat Intelligence Initiative and Invests in Machine Learning – WFMZ Allentown
MENLO PARK, Calif., Aug. 6, 2020 /PRNewswire/ -- Ensighten, the leader in client-side website security and privacy compliance enforcement, today announced increased investment into threat intelligence powered by machine learning. The new threat intelligence will focus specifically on client-side website threats with a mandate of discovering new methods as well as actively monitoring ongoing attacks against organizations.
Client-side attacks such as web skimming are now one of the leading threat vectors for data breaches and with a rapid acceleration of the digital transformation, businesses are facing a substantially increased risk. With privacy regulations, including the CCPA and GDPR, penalizing organizations for compromised customer data, online businesses of all sizes are facing significant security challenges due to the number of organized criminal groups using sophisticated malware.
"We have seen online attacks grow in both intensity and complexity over the past couple of years, with major businesses having their customers' data stolen," said Marty Greenlow, CEO of Ensighten. "One of the biggest challenges facing digital security is that these attacks happen at the client side in the customers' browser, making them very difficult to detect and often run for significant periods of time. By leveraging threat intelligence and machine learning, our customers will benefit from technology which dynamically adapts to the growing threat." Ensighten already provides the leading client-side website security solution to prevent accidental and malicious data leakage, and by expanding its threat intelligence, not only will it benefit its own technology, but also the security community in general. "We are a pioneer in website security, and we need to continue to lead the way," said Greenlow.
Ensighten's security technology is used by the digital marketing and digital security teams of some of the world's largest brands to protect their website and applications against malicious threats. This new threat intelligence initiative will enable further intelligence-driven capabilities and machine learning will drive automated rules, advanced data analytics, and more accurate identification. "Threat intelligence has always been part of our platform," said Jason Patel, Ensighten CTO, "but this investment will allow us to develop some truly innovative technological solutions to an issue that is unfortunately not only happening more regularly but is also growing in complexity."
Additional Resources
Learn more at http://www.ensighten.com or email info@ensighten.com
About Ensighten
Ensighten provides security technology to prevent client-side website data theft to the world's leading brands, protecting billions of online transactions. Through its cloud-based security platform, Ensighten continuously analyzes and secures online content at the point where it is most vulnerable: in the customer's browser. Ensighten threat intelligence focuses on client-side website attacks to provide the most comprehensive protection against web skimming, JavaScript Injection, malicious adware and emerging methods.
Here is the original post:
Ensighten Launches Client-Side Threat Intelligence Initiative and Invests in Machine Learning - WFMZ Allentown
Hey software developers, youre approaching machine learning the wrong way – The Next Web
I remember the first time I ever tried to learn to code. I was in middle school, and my dad, a programmer himself, pulled open a text editor and typed this on the screen:
Excuse me? I said.
It prints Hello World, he replied.
Whats public? Whats class? Whats static? Whats
Ignore that for now. Its just boilerplate.
But I was pretty freaked out by all that so-called boilerplate I didnt understand, and so I set out to learn what each one of those keywords meant. That turned out to be complicated and boring, and pretty much put the kibosh on my young coder aspirations.
Its immensely easier to learn software development today than it was when I was in high school, thanks to sites likecodecademy.com, the ease of setting up basic development environments, and a generalsway towards teaching high-level, interpreted languageslike Python and Javascript. You can go from knowing nothing about coding to writing your first conditional statements in a browser in just a few minutes. No messy environmental setup, installations, compilers, or boilerplate to deal with you can head straight to the juicy bits.
This is exactly how humans learn best. First, were taught core concepts at a high level, and onlythencan we appreciate and understand under-the-hood details and why they matter. We learn Python,thenC,thenassembly, not the other way around.
Unfortunately, lots of folks who set out to learn Machine Learning today have the same experience I had when I was first introduced to Java. Theyre given all the low-level details up front layer architecture, back-propagation, dropout, etc and come to think ML is really complicated and that maybe they should take a linear algebra class first, and give up.
Thats a shame, because in the very near future, most software developers effectively using Machine Learning arent going to have to think or know about any of that low-level stuff. Just as we (usually) dont write assembly or implement our own TCP stacks or encryption libraries, well come to use ML as a tool and leave the implementation details to a small set of experts. At that point after Machine Learning is democratized developers will need to understand not implementation details but instead best practices in deploying these smart algorithms in the world.
Today, if you want to build a neural network that recognizes your cats face in photos or predicts whether your next Tweet will go viral, youd probably set off to learn eitherTensorFloworPyTorch. These Python-based deep learning libraries are the most popular tools for designing neural networks today, and theyre both under 5 years old.
In its short lifespan, TensorFlow has already become way,waymore user-friendly than it was five years ago. In its early days, you had to understand not only Machine Learning but also distributed computing and deferred graph architectures to be an effective TensorFlow programmer. Even writing a simple print statement was a challenge.
Just earlier this fall, TensorFlow 2.0 officially launched, making the framework significantly more developer-friendly. Heres what a Hello-World-style model looks like in TensorFlow 2.0:
If youve designed neural networks before, the code above is straight-forward and readable. But if you havent or youre just learning, youve probably got some questions. Like, what is Dropout? What are these dense layers, how many do you need and where do you put them? Whatssparse_categorical_crossentropy? TensorFlow 2.0 removes some friction from building models, but it doesnt abstract away designing the actual architecture of those models.
So what will the future of easy-to-use ML tools look like? Its a question that everyone from Google to Amazon to Microsoft and Apple are spending clock cycles trying to answer. Also disclaimer it is whatIspend all my time thinking about as an engineer at Google.
For one, well start to see many more developers using pre-trained models for common tasks, i.e. rather than collecting our own data and training our own neural networks, well just use Googles/Amazons/Microsofts models. Many cloud providers already do something like this. For example, by hitting a Google Cloud REST endpoint, you can use a pre-trained neural networks to:
You can also run pre-trained models on-device, in mobile apps, using tools like GooglesML Kitor ApplesCore ML.
The advantage to using pre-trained models over a model you build yourself in TensorFlow (besides ease-of-use) is that, frankly, you probably cannot personally build a model more accurate than one that Google researchers, training neural networks on a whole Internet of data and tons GPUs andTPUs, could build.
The disadvantage to using pre-trained models is that they solve generic problems, like identifying cats and dogs in images, rather than domain-specific problems, like identifying a defect in a part on an assembly line.
But even when it comes to training custom models for domain-specific tasks, our tools are becoming much more user-friendly.
Screenshot of Teachable Machine, a tool for building vision, gesture, and speech models in the browser.
Googles freeTeachable Machinesite lets users collect data and train models in the browser using a drag-and-drop interface. Earlier this year, MIT released a similarcode-free interfacefor building custom models that runs on touchscreen devices, designed for non-coders like doctors.Microsoftand startups likelobe.aioffer similar solutions. Meanwhile,Google Cloud AutoMLis an automated model-training framework for enterprise-scale workloads.
As ML tools become easier to use, the skills that developers hoping to use this technology (but not become specialists) will change. So if youre trying to plan for where, Wayne-Gretsky-style, the puck is going, what should you study now?
What makes Machine Learning algorithms distinct from standard software is that theyre probabilistic. Even a highly accurate model will be wrong some of the time, which means its not the right solution for lots of problems, especially on its own. Take ML-powered speech-to-text algorithms: it might be okay if occasionally, when you ask Alexa to Turn off the music, she instead sets your alarm for 4 AM. Its not ok if a medical version of Alexa thinks your doctor prescribed you Enulose instead of Adderall.
Understanding when and how models should be used in production is and will always be a nuanced problem. Its especially tricky in cases where:
Take medical imaging. Were globally short on doctors and ML models are oftenmore accuratethan trained physicians at diagnosing disease. But would you want an algorithm to have the last say on whether or not you have cancer? Same thing with models that help judges decide jail sentences.Models can be biased, but so are people.
Understanding when ML makes sense to use as well as how to deploy it properly isnt an easy problem to solve, but its one thats not going away anytime soon.
Machine Learning models are notoriously opaque. Thats why theyre sometimes called black boxes. Its unlikely youll be able to convince your VP to make a major business decision with my neural network told me so as your only proof. Plus, if you dont understand why your model is making the predictions it is, you might not realize its making biased decisions (i.e. denying loans to people from a specific age group or zip code).
Its for this reason that so many players in the ML space are focusing on building Explainable AI features tools that let users more closely examine what features models are using to make predictions. We still havent entirely cracked this problem as an industry, but were making progress. In November, for example, Google launched a suite of explainability tools as well as something calledModel Cards a sort of visual guide for helping users understand the limitations of ML models.
Googles Facial Recognition Model Card shows the limitations of this particular model.
There are a handful of developers good at Machine Learning, a handful of researchers good at neuroscience, and very few folks who fall in that intersection. This is true of almost any sufficiently complex field. The biggest advances well see from ML in the coming years likely wont be from improved mathematical methods but from people with different areas of expertise learning at least enough Machine Learning to apply it to their domains. This is mostly the case in medical imaging, for example, where themost exciting breakthroughs being able to spot pernicious diseases in scans are powered not by new neural network architectures but instead by fairly standard models applied to a novel problem. So if youre a software developer lucky enough to possess additional expertise, youre already ahead of the curve.
This, at least, is whatIwould focus on today if I were starting my AI education from scratch. Meanwhile, I find myself spending less and less time building custom models from scratch in TensorFlow and more and more time using high-level tools like AutoML and AI APIs and focusing on application development.
This article was written by Dale Markowitz, an Applied AI Engineer at Google based in Austin, Texas, where she works on applying machine learning to new fields and industries. She also likes solving her own life problems with AI, and talks about it on YouTube.
Originally posted here:
Hey software developers, youre approaching machine learning the wrong way - The Next Web
Introducing The AI & Machine Learning Imperative – MIT Sloan
Topics The AI & Machine Learning Imperative
The AI & Machine Learning Imperative offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.
Leading organizations recognize the potential for artificial intelligence and machine learning to transform work and society. The technologies offer companies strategic new opportunities and integrate into a range of business processes customer service, operations, prediction, and decision-making in scalable, adaptable ways.
As with other major waves of technology, AI requires organizations and managers to shed old ways of thinking and grow with new skills and capabilities. The AI & Machine Learning Imperative, an Executive Guide from MIT SMR, offers new insights from leading academics and practitioners in data science and AI. The guide explores how managers and companies can overcome challenges and identify opportunities across three key pillars: talent, leadership, and organizational strategy.
Email Updates on AI, Data & Machine Learning
Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations.
Please enter a valid email address
Thank you for signing up
Privacy Policy
The series launches Aug. 3, and summaries of the upcoming articles are included below. Sign up to be reminded when new articles launch in the series, and in the meantime, explore our recent library of AI and machine learning articles.
In order to achieve the ultimate strategic goals of AI investment, organizations must broaden their sights beyond creating augmented intelligence tools for limited tasks. To prepare for the next phase of artificial intelligence, leaders must prioritize assembling the right talent pipeline and technology infrastructure.
Recent technical advances in AI and machine learning offer genuine productivity returns to organizations. Nevertheless, finding and enabling talented individuals to succeed in engineering these kinds of systems can be a daunting challenge. Leading a successful AI-enabled workforce requires key hiring, training, and risk management considerations.
AI is no regular technology, so AI strategy needs to be approached differently than regular technology strategy. A purposeful approach is built on three foundations: a robust and reliable technology infrastructure, a specific focus on new business models, and a thoughtful approach to ethics. Available Aug. 10.
CFOs who take ownership of AI technology position themselves to lead an organization of the future. While AI is likely to impact business practices dramatically in the future across the C-suite, its already having an impact today and the time for CFOs to step up to AI leadership is now. Available Aug. 12.
To remain relevant and resilient, companies and leaders must strive to build business models in a way that ensures three key components are working together: AI that enables and powers a centralized data lake of enterprise data, a marketplace of sellers and partners that make individualized offers based on the intelligence of the data collected and powered by AI, and a SaaS platform that is essential for users. Available Aug. 17.
Acquiring the right AI technology and producing results, while critical, arent enough. To gain value from AI, organizations need to focus on managing the gaps in skills and processes that impact people and teams within the organization. Available Aug. 19.
The AI & Machine Learning Imperative offers new insights from leading academics and practitioners in data science and artificial intelligence. The Executive Guide, published as a series over three weeks, explores how managers and companies can overcome challenges and identify opportunities by assembling the right talent, stepping up their own leadership, and reshaping organizational strategy.
Ally MacDonald (@allymacdonald) is a senior editor at MIT Sloan Management Review.
Read the original post:
Introducing The AI & Machine Learning Imperative - MIT Sloan
Who Does the Machine Learning and Data Science Work? – Customer Think
A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions. Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals.
We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g., gathering data, analyzing data, communicating to stakeholders) to complete those projects. Todays post will focus on the broad work activities (or projects) that make up their roles at work, including Build prototypes to explore applying machine learning to new areas and Analyze and understand data to influence product or business decisions. Toward that end, I will use the data from the recent Kaggle survey of over 19,000 data professionals in which respondents were asked a variety of questions about their analytics practices, including their job title, work experience and the tools and products they use.
The survey respondents were asked to Select any activities that make up an important part of your role at work: (Select all that apply). On average respondents indicated that two (median) of the activities make up on important part of their role. The entire list of activities (shown in Figure 1) were:
Figure 1. Activities that Make Up Important Parts of Data Professionals Role
The The top work activity was somewhat practical in nature, helping the company improve how it runs the business: analyzing data to influence products and decisions. The work activity with the lowest endorsement was more theoretical in nature: doing research that advances the state of the art of machine learning.
Next, I examined if there were differences across different data roles (as indicated by respondents job title) with respect to work activities. I looked at 5 different job title for this analysis. The results revealed a couple of interesting findings (See Figure 2):
First, respondents who self-identified as Data Scientists, on average, indicated that they are involved in 3 (median) activities at work compared to the other respondents who are involved in 2 job activities.
Second, we see that the profile of work activities varies greatly across different data roles. While many of the respondents indicated that analysis and understanding of data to influence products/decisions was the top activity for them, a top activity for Research Scientists was doing research that advances the state of the art of machine learning. Additionally, the top activity for Data Engineers was building and/or running the data infrastructure.
Figure 2. Typical work activities vary across different data roles.
The top work activity for data professional roles appears to be very practical and necessary to run day-to-day business operations. These top work activities included influencing business decisions, building prototypes to expand machine learning to new areas and improving ML models. The bottom activity was more about long-term understanding of machine learning reflected in conducting research to advance the state of the art of machine learning.
Different data roles possess different activity profiles. Top work activities tend to be associated with the skill sets of different data roles. Building/Running data infrastructure was the top activity for Data Engineers; doing research to advance the field of machine learning was a top activity for Research Scientists.These results are not surprising as we know that different data professionals have different skill sets. In prior research, I found that data professionals who self-identified as Researchers have a strong math/statistics/research skill set. Developers, on the other hand, have strong programming/technology skills. And data professionals who were Domain Experts have strong business-domain knowledge. Data science and machine learning work really is a team sport. Getting data teams with members who have complementary skill sets will likely improve the success rate of data science projects.
Remember that data professionals have their unique skill set that makes them a better fit for some data roles than others.When applying for data-related positions, it might be useful to look at the type of work activities for which you have experience (or are competent) and apply for the positions with corresponding job titles. For example, if you are proficient in running a data infrastructure, you might consider focusing on Data Engineer jobs. If you have a strong skill set related to research and statistics, you might be more likely to get a call back when applying for Research Scientist positions.
The rest is here:
Who Does the Machine Learning and Data Science Work? - Customer Think
Artificial Intelligence and Machine Learning Path to Intelligent Automation – Embedded Computing Design
With evolving technologies, intelligent automation has become a top priority for many executives in 2020. Forrester predicts the industry will continue to grow from $250 million in 2016 to $12 billion in 2023. With more companies identifying and implementation the Artificial Intelligence (AI) and Machine Learning (ML), there is seen a gradual reshaping of the enterprise.
Industries across the globe integrate AI and ML with businesses to enable swift changes to key processes like marketing, customer relationships and management, product development, production and distribution, quality check, order fulfilment, resource management, and much more. AI includes a wide range of technologies such as machine learning, deep learning (DL), optical character recognition (OCR), natural language processing (NLP), voice recognition, and so on, which creates intelligent automation for organizations across multiple industrial domains when combined with robotics.
Let us see how some of these technologies help industries globally to implement automation.
Machine learning has recently been applied to detect anomalies in manufacturing processes. Using machine learning, health monitoring of the equipment can be automated where the specialties of the sensor devices data like vibrations, sound, temperature, etc. from the collected data can be learned through training.
This is useful to identify early wear and tear of equipment and avoid catastrophic damage. It can catch the smallest flaw that the human eye may miss. Techniques can be selected depending on the type of attributes required to extract the features and based on the features various machine learning algorithms can be applied to detect the anomalies.
One of the main tasks of any machine learning algorithm in the self-driving car is a continuous rendering of the surrounding environment and the prediction of possible changes to those surroundings. It is essential for autonomous cars to recognize objects or pedestrians on the road, irrespective whether it is day or night. For the success of autonomous cars, automobile companies integrate advanced driver assist systems (ADAS) with thermal imaging.
By executing deep learning algorithms on the image data set that are captured by thermal cameras, it is possible to identify pedestrians in any weather condition. It can cover a larger or small part of the image based on distance. There are few deep learning algorithms like Fast R-CNN or YOLO that can help achieve this automation making autonomous cars safer and efficient on roads.
OCR is another technology which uses deep learning to recognize characters. It is of great use in manufacturing to automate processes which are subject to human errors due to fatigue or casual behavior. These activities include verifications of lot code, batch code, expiry date etc. Various CNN architectures like LeNet, Alexnet etc. can be used for this automation and it can also be customized to achieve the desired accuracy.
Loaning money is a huge business for financial institutions. The value and approval of the loans is entirely based on how likely an individual or business will be able to repay. Determining creditworthiness is most important decision for this business to succeed. Along with credit score various other parameters are considered for making such decisions which makes the whole process very complex and time consuming.
To save on time and accelerate the process, trained machine learning algorithms can be used to predict and classify the creditworthiness of the applicant. This can simplify the classification of applicants and improve decision making for loan sanction.
AI and ML is creating a new vision of machine-human collaboration and taking businesses to new levels. Machine learning helps organizations across various industrial domains to develop intelligent solutions based on proprietary or open source algorithms/frameworks that processes data and runs sophisticated algorithms on cloud and edge. Machine Learning models can be built, trained, validated, optimized, deployed and tested using latest tools and technologies. This ensures faster decision making, increased productivity, business process automation, and faster anomaly detection for the businesses.
Kaumil Desai is associated withVOLANSYSas a Delivery Manager past 3 years. He has vast experience in product development, Machine Learning on edge, complex algorithms design & development for various industries including Industrial Automation, Electrical safety, Telecom, etc.
See original here:
Artificial Intelligence and Machine Learning Path to Intelligent Automation - Embedded Computing Design
Blacklight Solutions Unveils Software to Simplify Business Analytics with AI and Machine Learning – PRNewswire
AUSTIN, Texas, Aug. 5, 2020 /PRNewswire/ -- Blacklight Solutions, an applied analytics company based in Texas, introduced today a simplified business analytics platform that allows small to mid-market businesses to implement artificial intelligence and machine learning with code free transformation, aggregation, blending and mixing of multiple data sources. Blacklight software empowers companies to increase efficiency by using machine learning and artificial intelligence for business processes with a team of experts guiding this metamorphosis.
"Small and mid-size firms need a simpler way to leverage these technologies for growth in the way large enterprises have." said Chance Coble, Blacklight Solutions CEO. "We are thrilled to bring an easy pay-as-you-go solution along with the expertise to guide them and help them succeed."
Blacklight Solutions believes that now more than ever companies need business analytics solutions that can increase sales, enhance productivity, and improve risk control. Blacklight software gives small to mid-market businesses an opportunity to implement the latest technology and create insightful digital products without requiring a dedicated team or familiarity with coding languages. Blacklight Solutions provides each client with a team of experts to help guide their journey in becoming evidence-based decision makers.
Capabilities and Benefits for Users
Blacklight is a cloud-based system that is built to scale with your business as it grows. It is the simplest way to create business analytics solutions that users can then sell to their customers. Users have the added ability to create dashboards and embed them in client facing portals. Additionally, users are enabled to grow and improve cash flow by creating data products that their customers can subscribe to resulting in generated revenue. Blacklight software also features an alerting system that notifies designated users when changes in data or anomalies occur.
"Blacklight brought the strategy, expertise and software that made analytics a solution for us to achieve new business objectives and grow sales," said Deren Koldwyn, CEO, Avannis, Blacklight Solutions client.
Blacklight software brings the full power of business analytics to companies that are looking for digital transformations and want to move fast. Blacklight Solutions is the only full-service solution that provides empowering software combined with the insight and strategy necessary for impactful analytics implementations. To learn more about Blacklight Solutions' offerings visit http://www.blacklightsolutions.com.
About Blacklight Solutions
Blacklight Solutions is an analytics firm focused on helping mid-market companies accelerate their growth. Founded in 2009, Blacklight Solutions has spent over a decade helping organizations solve business problems by putting their data to work to generate revenue, increase efficiency and improve customer relationships.
Media Contact:
Bailey Steinhauser979.966.8170[emailprotected]
SOURCE Blacklight Solutions
AI is learning when it should and shouldnt defer to a human – MIT Technology Review
The context: Studies show that when people and AI systems work together, they can outperform either one acting alone. Medical diagnostic systems are often checked over by human doctors, and content moderation systems filter what they can before requiring human assistance. But algorithms are rarely designed to optimize for this AI-to-human handover. If they were, the AI system would only defer to its human counterpart if the person could actually make a better decision.
The research: Researchers at MITs Computer Science and AI Laboratory (CSAIL) have now developed an AI system to do this kind of optimization based on strengths and weaknesses of the human collaborator. It uses two separate machine-learning models; one makes the actual decision, whether thats diagnosing a patient or removing a social media post, and one predicts whether the AI or human is the better decision maker.
The latter model, which the researchers call the rejector, iteratively improves its predictions based on each decision makers track record over time. It can also take into account factors beyond performance, including a persons time constraints or a doctors access to sensitive patient information not available to the AI system.
Read the original here:
AI is learning when it should and shouldnt defer to a human - MIT Technology Review