Category Archives: Machine Learning

The National Geospatial-Intelligence Agency is well known for analysis of imagery and maps, but text, or written language, is a key part of the process. In a year-long study, members of the NGA workforce reported that text reading and generation occupy up to 80% of their average workflow, whether in conducting research, reviewing documents, tipping imagery or generating reports.

NGA conducted the study of natural language processing through a federally funded research and development center, with hopes to significantly raise awareness of the potential time savings and intelligence gains made possible through greater access to text analytics software.

If a picture is worth a thousand words, NGA is in the business of countless words, says Monica Lipscomb of NGA Research, who serves as the NLP program manager. Map reading, legend generation, and image notation are obvious examples.

Natural language processing, also known as human language technology, enables the automated sifting, sorting, translating, comprehending and sensemaking of billions of words.In addition to speeding the analytic workflow, NLP has applicability to workflows involving security, finance, policy, records management and safety of navigation alerts, according to Lipscomb. The Source Maritime Automated Processing System, launched in early 2022, is driven by natural language processing and basic machine learning. SMAPS has reportedly cut in half the time needed to process incoming incident messages and generate alerts.

Lipscomb says the agency wants to facilitate mission advancement in other NGA workflows akin to those achieved through SMAPS.

Many NGA employees know that NLP resources are available, but they have difficulty knowing where to find them or how to orient them towards NGA topics of interest, she said.

As a next step, NGA will discuss natural language processing resources available throughout the Intelligence Community and generate an enterprise-wide community of interest.

Read more at NGA

See original here:
NGA Puts Machine Learning to Work to Speed Mission, Further Research - HS Today - HSToday

The singularity is coming for us, day by creeping day. Artificial intelligence is starting to write about cycling. It is starting to create pictures of cycling. And now, it is starting to predict the results of races that havent even happened yet.

There are humans involved at some point there always are, before the end of everything. In this case, it is a data and analytics consultancy called Decision Inc., Australia. The humans developed the modelling, fed it to their machine learning tool, let it marinate for a bit [that may be creative license] and then, the magic happened.

Machine Learning is a form of Artificial Intelligence which uses advanced data analytics [to] solve complex issues, explained Decision Inc, Australia CEO, Aiden Heke. It uses algorithms to best imitate how humans solve problems or predict outcomes.

Since the technology has evolved so much over the past few decades, we thought: why not use it to predict the outcome of the UCI World Championships?

First up, the womens road race:

A caveatthe Machines were crunching their numbers before Annemiek van Vleuten crashed out of the mixed team time trial, putting her start at risk. Also, apparently The Machines dont rate Grace Brown as a top 10 favourite. But all that aside? Those are certainly some credible names.

To the men:

Again, some curiosities in here for me. The podium seems credible, but I think Van der Poel is a bit more of a dark horse than this is letting on. Pogaar seems low; Almeida seems high. Im also furious about the Juraj Sagan erasure, but that is a me thing, not a you thing, and certainly not an AI thing.

Decision Inc. is likening their cycling foray to Deep Blue, an early machine learning venture from the mid-1990s that famously vanquished chess grandmaster Garry Kasparov. Its why were putting it to the test, to see just how far its come, said Decision Inc. CEO Aiden Heke. Were keen for everyone who fancies themselves as a bit of an expert on cycling to see if they can win where Kasparov couldnt: against the Machine.

If you want to show that you know more about this weekends cycling than a series of computer calculations, you can head to the companys Instagram account where you could win some signed cycling goodies.

Or, you can just wade into the comments here and tell us who your pick is. Thatd be fun too.

See original here:
Machine learning has predicted the winners of the Worlds - CyclingTips

Nearly 20% of pregnancies in people with lupus result in an adverse pregnancy outcome (APO). In a new study, scientists were able to improve prediction accuracy of APOs using machine learning. Machine learning refers to the process by which a computer is able to improve its own performance by continuously incorporating new data into an existing statistical model.

Using a previously developed APO prediction model utilizing data from a larger multi-center, multi-ethnic study of lupus pregnancies known as the Predictors of pRegnancy Outcome: bioMarkers In Antiphospholid Antibody Syndrome and the Systemic Lupus Erythematosus (PROMISSE) study, and statistical analysis coupled with machine learning, researchers analyzed data from 385 women in their first trimester of pregnancy. They identified lupus anticoagulant positivity, disease assessment score, diastolic blood pressure or resting heartbeat, current use of antihypertension medication, and platelet count as significant baseline predictors of APO.

Researchers suggest that the ability to identify, lupus patients at high risk of APO early in pregnancy, could enhance the capacity to manage these patients and conduct trials of new treatments to prevent pre-eclampsia and placental insufficiency.

Further studies to identify new biomarkers and risk factors for APO are still needed. The Lupus Foundation of America provided the study author, Jane Salmon, MD, with a three-year grant for her IMPACT study, the first trial of a biologic therapy to prevent adverse pregnancy outcomes in high-risk pregnancies in patients with antiphospholipid syndrome (APS) with or without systemic lupus erythematosus (SLE), which also helped support this new research. Learn more about lupus and pregnancy.

Read the study

Read the original post:
Machine Learning Can Be Used to Improve the Ability to Predict Adverse Pregnancy Outcomes in Women with Lupus - Lupus Foundation of America

Using AI, machine learning and advanced analytics to protect and optimize business | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

See original here:
Using AI, machine learning and advanced analytics to protect and optimize business - Security Magazine

There is a high demand for machine learning engineer jobs, but the hiring process is tough to crack. Companies want to hire professionals with experience in dealing with various machine learning problems.

For a newbie or fresh graduate, there are only a few ways to showcase skills and experience. They can either get an internship, work on open source projects, volunteer in NGO projects, or work on portfolio projects.

In this post, we will be focusing on machine learning portfolio projects that will boost your resume and help you during the recruitment process. Working solo on the project also makes you better at problem-solving.

mRNA Degradation project is a complex regression problem. The challenge in this project is to predict degradation rates that can help scientists design more stable vaccines in the future.

The project is 2 years old, but you will learn a lot about solving regression problems using complex 3D data manipulation and deep learning GRU models. Furthermore, we will be predicting 5 targets: reactivity, deg_Mg_pH10, deg_Mg_50C, deg_pH10, deg_50C.

Automatic Image Captioning is the must-have project in your resume. You will learn about computer vision, CNN pre-trained models, and LSTM for natural language processing.

In the end, you will build the application on Streamlit or Gradio to showcase your results. The image caption generator will generate a simple text describing the image.

You can find multiple similar projects online and even create your deep learning architecture to predict captions in different languages.

The primary purpose of the portfolio project is to work on a unique problem. It can be the same model architecture but a different dataset. Working with various data types will improve your chance of getting hired.

Forecasting using Deep Learning is a popular project idea, and you will learn many things about time series data analysis, data handling, pre-processing, and neural networks for time-series problems.

The time series forecasting is not simple. You need to understand seasonality, holiday seasons, trends, and daily fluctuation. Most of the time, you dont even require neural networks, and simple linear regression can provide you with the best-performing model. But in the stock market, where the risk is high, even a one percent difference means millions of dollars in profit for the company.

Having a Reinforcement Learning project on your resume gives you an edge during the hiring process. The recruiter will assume that you are good at problem-solving and you are eager to expand your boundaries to learn about complex machine learning tasks.

In the Self-Driving car project, you will train the Proximal Policy Optimization (PPO) model in the OpenAI Gym environment (CarRacing-v0).

Before you start the project, you need to learn the fundamentals of Reinforcement Learning as it is quite different from other machine learning tasks. During the project, you will experiment with various types of models and methodologies to improve agent performance.

Conversational AI is a fun project. You will learn about Hugging Face Transformers, Facebook Blender Bot, handling conversational data, and creating chatbot interfaces (API or Web App).

Due to the huge library of datasets and pre-trained models available on Hugging Face, you can basically finetune the model on a new dataset. It can be Rick and Morty conversation, your favorite film character, or any celebrity that you love.

Apart from that you can improve the chatbot for your specific use case. In case of medical application. The chatbot needs technical knowledge and understands the patient's sentiment.

Automatic Speech Recognition is my favorite project ever. I have learned everything about transformers, handling audio data, and improving the model performance. It took me 2 months to understand the fundamentals and another two to create the architecture that will work on top of the Wave2Vec2 model.

You can improve the model performance by boosting Wav2Vec2 with n-grams and text pre-processing. I have even pre-processed the audio data to improve the sound quality.

The fun part is that you can fine-tune the Wav2Vec2 model on any type of language.

End-to-end machine learning project experience is a must. Without it, your chance of getting hired is pretty slim.

You will learn:

The main purpose of this project is not about building the best model or learning new deep learning architecture. The main goal is to familiarize the industry standards and techniques for building, deploying, and monitoring machine learning applications. You will learn a lot about development operations and how you can create a fully automated system.

After working on a few projects, I will highly recommend you create a profile on GitHub or any code-sharing site where you can share your project findings and documentation.

The principal purpose of working on a project is to improve your odds of getting hired. Showcasing the projects and presenting yourself in front of a potential recruiter is a skill.

So, after working on a project, start promoting it on social media, create a fun web app using Gradio or Streamlit, and write an engaging blog. Dont think about what people are going to say. Just keep working on a project and keep sharing. And I am sure in no time multiple recruiters will approach you for the job.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.

Read the original:
7 Machine Learning Portfolio Projects to Boost the Resume - KDnuggets

ARLINGTON, Va. U.S. military researchers are asking industry to develop computers able not only to analyze large amounts of data automatically, but also communicate and cooperate with humans to resolve ambiguities and improve performance over time.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001122S0052) on Thursday for the Environment-driven Conceptual Learning (ECOLE) project.

From industry, the DARPA ECOLE project seeks proposals in five areas: human language technology; computer vision; artificial intelligence (AI); reasoning; and human-computer interaction.

ECOLE will create AI agents able to learn from linguistic and visual input to enable humans and computers to work together to analyze image, video, and multimedia documents quickly in missions where reliability and robustness are essential.

Related: Military researchers to apply artificial intelligence (AI) and machine learning to combat medical triage

ECOLE will develop algorithms that can identify, represent, and ground the attributes that form the symbolic and contextual model for a particular object or activity through interactive machine learning with a human analyst. Knowledge of attributes and affordances, learned dynamically from data encountered within an analytic workflow, will enable joint reasoning with a human partner.

This acquired knowledge also will enable the machine to recognize never-before-seen objects and activities without misclassifying them as a member of a previously learned class, detect changes in known objects, and report these changes when they are significant.

System interaction with human intelligence analysts is expected to be symbiotic, with the systems augmenting human cognitive capabilities while simultaneously seeking instruction and correction to achieve accuracy.

Industry proposals should specify how symbolic knowledge representations will be acquired from unlabeled data, including the specifics of the learning mechanism; how these representations will be associated and reasoned within a growing body of knowledge; how the representations will be applied to human-interpretable object and activity recognition; and how the framework will permit collaboration with several analysts to resolve ambiguity, extend the set of known representations, and provide greater recognitional accuracy and coverage.

Related: Artificial intelligence (AI) to enable manned and unmanned vehicles adapt to unforeseen events like damage

The four-year ECOLE project with three phases; this solicitation concerns only the first and second phases. The first phase will create prototype agents that can pull relevant information out of unlabeled multimedia data, supplemented with human interaction.

These prototypes will demonstrate not only the ability to learn new concepts, but also to recombine previously learned attributes to recognize never-before-seen objects and activities. Systems also will be able to reason over similarities and differences in objects and activities.

The second phase of the ECOLE project will scale-up the framework to include several AI agents and human analysts to help deal with uncertain or contradictory information.

Computer interaction with human analysts will enable the system to learn to name and describe objects, actions, and properties to verify and augment their representations, and to acquire complex knowledge quickly and accurately from potentially sparse observations.

Related: Wanted: artificial intelligence (AI) and machine autonomy algorithms for military command and control

Humans and computers will work together primarily through the English language -- including words with several different meanings -- in a way that is readily understandable. The ECOLE project also will have two technical areas: distributed curriculum learning; and human-machine collaborative analysis.

Distributed curriculum learning involves multimedia data, and will use human partners provide feedback on the learning process. human-machine collaborative analysis will involve a human-machine interface (HMI) to improve ECOLE representations and analyze data such as multimedia and social media.

Companies interested should upload abstracts no later than 29 Sept. 2022, and full proposals by 14 Nov. 2022 to the DARPA BAA website at https://baa.darpa.mil.

Email questions or concerns to DARPA at ECOLE@darpa.mil. More information is online at https://sam.gov/opp/fd50cb65daf5493d886fa1ddc2c0dd77/view.

View original post here:
Wanted: artificial intelligence (AI) and machine learning to help humans and computers work together - Military & Aerospace Electronics