Category Archives: Machine Learning

LONDON--(BUSINESS WIRE)--causaLens, a deep-tech company predicting and optimising the global economy, has released the Worlds first causal Artificial Intelligence (causal AI) enterprise platform. Businesses no longer have to rely on curve-fitting machine learning platforms unable to handle the complexity of today's world. They are invited to join the real AI revolution with a platform that understands cause and effect.

The causaLens platform defines a new category of machine intelligence. Its next generation AI engine harnesses an understanding of cause and effect relationships to directly optimise business KPIs.

Businesses investing in the current form of machine learning (ML), including AutoML, have just been paying to automate a process that fits curves to data without an understanding of the real world. They are effectively driving forward by looking in the rear-view mirror, explains causaLens CEO Darko Matovski. Our platform takes a radically different approach. Causal AI teaches machines to understand cause and effect, a necessary step to developing true AI. This allows our platform to autonomously operate at a new level of abstraction that explains to businesses what actions they need to take to achieve their objectives.

causaLens has a track record of breaking new ground, having pioneered automated machine learning (AutoML) for time series data. The causal AI platform retains the advantages of comprehensive automation, allowing thousands of data sets to be cleaned, sorted and monitored at the same time. However, it combines it with causal models and insights that are truly explainable - traditionally the sole province of domain experts. Unique human knowledge is harnessed through intuitive interfaces for human-machine partnerships.

Since its inception in 2017, causaLens has worked with a range of corporates across multiple industries. Customers include some of the worlds largest Asset Managers, Hedge Funds, Tier-1 Investment Banks, Transportation and Logistics companies, and Energy and Commodity traders.

Masami Johnstone, Head of Information Services at CLS, whose products help clients navigate the changing Foreign Exchange marketplace, said: "The causaLens platform has enabled us to discover additional value in our data. Their causal AI technology autonomously finds valuable signals in huge datasets and has helped us to understand relationships between our data and other datasets.

Todays world is changing faster than ever before. Current state of the art ML barely scratches the surface of what machines can do. Causal AI is the next huge step forward.

Demonstrations of the product can be requested via causaLens.com.

causaLens

causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect - a major step towards true AI. Its enterprise platform is used to transform leading businesses in Finance, IoT, Energy, Telecommunications and others.

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causaLens launches the first causal AI platform - Business Wire

Latest Research Report: Machine Learning in Education industry

Machine Learning in Education Market report is to provide accurate and strategic analysis of the Profile Projectors industry. The report closely examines each segment and its sub-segment futures before looking at the 360-degree view of the market mentioned above. Market forecasts will provide deep insight into industry parameters by accessing growth, consumption, upcoming market trends and various price fluctuations.

This has brought along several changes in This report also covers the impact of COVID-19 on the global market.

Machine Learning in Education Market competition by top manufacturers as follow: , IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning

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Global Machine Learning in Education Market research reports growth rates and market value based on market dynamics, growth factors. Complete knowledge is based on the latest innovations in the industry, opportunities and trends. In addition to SWOT analysis by key suppliers, the report contains a comprehensive market analysis and major players landscape.The Type Coverage in the Market are: Cloud-BasedOn-Premise

Market Segment by Applications, covers:Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers

Market segment by Regions/Countries, this report coversNorth AmericaEuropeChinaRest of Asia PacificCentral & South AmericaMiddle East & Africa

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Machine Learning in Education Market Incredible Possibilities, Growth Analysis and Forecast To 2025 - The Daily Chronicle

Model governance indicates the overall framework of how an organization control its model development and deployment workflow, including rules, protocols, and controls for machine learning models during production for example, access control, testing, validation, and the tracing of model results.

Although machine learning projects impact organisations, they dont always arrive at their full potential due to inefficiencies and mismanagement in the process. Machine governance is a priority for organisations to get the highest possible return on its machine learning investment.

Model governance indicates the overall framework of how an organization control its model development and deployment workflow, including rules, protocols, and controls for machine learning models during production for example, access control, testing, validation, and the tracing of model results. Tracking the model outcomes permits biases to be detected and rectified. It is important for models, which are programmed to learn as they may accidentally become biased that could bring out inaccurate or unethical results.

It is crucial for risk involved models to manage financial portfolios. As these models can impact on an individual or organizations finances directly, it is essential to verify and correct any biases or incorrect learning within the model.

As machine learning is a relatively new discipline, there are still a lot of inefficiencies that require to be advocated in ML processes. Machine learning projects can be missing essential value without model governance in place.

Clearing risk of model governance is vital to ensure that models involved with finances stay out of dangerous hazards. These models are programmed to continue learning along the run. However, these can understand biases if these are served with data. Datasets are capable of creating a bias which affects the decisions the model makes from that point on.

Model governance enables models to be audited and examined for speed, accuracy, and drift during production. It neglects any issues of model bias or inaccuracy, permitting models with risks involved to function smoothly.

Here are a few cases listed below to analyse the importance of model governance:

As mentioned before, the most glaring instance of why model governance is crucial in finance, but other industries require model governance as well. Banking industry uses machine learning models for many different processes that can be operated manually like credit scoring, interest rate risk modelling, and derivatives pricing.

Credit scoring models aid finance/ bank industry to make decisions in the loan approval process by delivering predictive analysis information concerning the potential for default or delinquency. It helps the bank to determine the risk costing they should use for the loan.

Interest rate risk models surveil earnings exposure to a range of potential market conditions and rate change to measure risk. The purpose of the model is to provide an overview of the potential dangers of the account it is monitoring.

These models estimate the value of assets by delivering a methodology for determining the cost of new products as well as complex products without market observations readily available. It is helpful for both the banks and investors to determine whether a business is worth investing in or not.

Serverless micro-service architecture for machine learning, algorithmia makes it the fastest route from development to deployment. It allows organizations to govern their machine learning operations securely with a healthy machine learning lifecycle. It manages MLOps with access controls to secure and audit machine learning models in production. Model governance algorithmias one of the benefits which ensures model accuracy by governing models and testing for speed, accuracy and drift.

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What is Model Governance and How it Works for Enterprises? - Analytics Insight

Overview Of Machine Learning in Medical Imaging Industry 2020-2025:

This has brought along several changes in This report also covers the impact of COVID-19 on the global market.

The Machine Learning in Medical Imaging Market analysis summary by Reports Insights is a thorough study of the current trends leading to this vertical trend in various regions. Research summarizes important details related to market share, market size, applications, statistics and sales. In addition, this study emphasizes thorough competition analysis on market prospects, especially growth strategies that market experts claim.

Machine Learning in Medical Imaging Market competition by top manufacturers as follow: , Zebra, Arterys, Aidoc, MaxQ AI, Google, Tencent, Alibaba,

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The global Machine Learning in Medical Imaging market has been segmented on the basis of technology, product type, application, distribution channel, end-user, and industry vertical, along with the geography, delivering valuable insights.

The Type Coverage in the Market are: Supervised LearningUnsupervised LearningReinforced Leaning

Market Segment by Applications, covers:BreastLungNeurologyCardiovascularLiverOthers

Market segment by Regions/Countries, this report coversNorth AmericaEuropeChinaRest of Asia PacificCentral & South AmericaMiddle East & Africa

Major factors covered in the report:

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The analysis objectives of the report are:

Access full Report Description, TOC, Table of Figure, Chart, [emailprotected] https://reportsinsights.com/industry-forecast/Machine-Learning-in-Medical-Imaging-Market-13318

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Reports Insights is the leading research industry that offers contextual and data-centric research services to its customers across the globe. The firm assists its clients to strategize business policies and accomplish sustainable growth in their respective market domain. The industry provides consulting services, syndicated research reports, and customized research reports.

Contact US:

:(US) +1-214-272-0234

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Machine Learning in Medical Imaging Market Incredible Possibilities, Growth Analysis and Forecast To 2025 - The Daily Chronicle

Kate MacDonald, New Zealand Government Fellow at the World Economic Forum, and Lofred Madzou, Project Lead, AI and Machine Learning at the World Economic Forum have published a report that explains how AI can benefit everyone.

According to MacDonald and Madzou, artificial intelligence can improve the daily lives of just about everyone, however, we still need to address issues such as accuracy of AI applications, the degree of human control, transparency, bias and various privacy issues. The use of AI also needs to be carefully and ethically managed, MacDonald and Madzou recommend.

As mentioned in a blog post by MacDonald and Madzou:

One way to [ensure ethical practice in AI] is to set up a national Centre for Excellence to champion the ethical use of AI and help roll out training and awareness raising. A number of countries already have centres of excellence those which dont, should.

The blog further notes:

AI can be used to enhance the accuracy and efficiency of decision-making and to improve lives through new apps and services. It can be used to solve some of the thorny policy problems of climate change, infrastructure and healthcare. It is no surprise that governments are therefore looking at ways to build AI expertise and understanding, both within the public sector but also within the wider community.

As noted by MacDonald and Madzou, the UK has established many Office for AI centers, which aim to support the responsible adoption of AI technologies for the benefit of everyone. These UK based centers ensure that AI is safe through proper governance, strong ethical foundations and understanding of key issues such as the future of work.

The work environment is changing rapidly, especially since the COVID-19 outbreak. Many people are now working remotely and Fintech companies have managed to raise a lot of capital to launch special services for professionals who may reside in a different jurisdiction than their employer. This can make it challenging for HR departments to take care of taxes, compliance, and other routine work procedures. Thats why companies have developed remote working solutions to support companies during these challenging times.

Many firms might now require advanced cybersecurity solutions that also depend on various AI and machine learning algorithms.

The blog post notes:

AI Singapore is bringing together all Singapore-based research institutions and the AI ecosystem start-ups and companies to catalyze, synergize and boost Singapores capability to power its digital economy. Its objective is to use AI to address major challenges currently affecting society and industry.

As covered recently, AI and machine learning (ML) algorithms are increasingly being used to identify fraudulent transactions.

As reported in August 2020, the Hong Kong Institute for Monetary and Financial Research (HKIMR), the research segment of the Hong Kong Academy of Finance (AoF), had published a report on AI and banking. Entitled Artificial Intelligence in Banking: The Changing Landscape in Compliance and Supervision, the report seeks to provide insights on the long-term development strategy and direction of Hong Kongs financial industry.

In Hong Kong, the use of AI in the banking industry is said to be expanding including front-line businesses, risk management, and back-office operations. The tech is poised to tackle tasks like credit assessments and fraud detection. As well, banks are using AI to better serve their customers.

Policymakers are also exploring the use of AI in improving compliance (Regtech) and supervisory operations (Suptech), something that is anticipated to be mutually beneficial to banks and regulators as it can lower the burden on the financial institution while streamlining the regulator process.

The blog by MacDonald and Madzou also mentions that India has established a Centre of Excellence in AI to enhance the delivery of AI government e-services. The blog noted that the Centre will serve as a platform for innovation and act as a gateway to test and develop solutions and build capacity across government departments.

The blog post added that Canada is notably the worlds first country to introduce a National AI Strategy, and to also establish various centers of excellence in AI research and innovation at local universities. The blog further states that this investment in academics and researchers has built on Canadas reputation as a leading AI research hub.

MacDonald and Madzou also mentioned that Malta has launched the Malta Digital Innovation Authority, which serves as a regulatory body that handles governmental policies that focus on positioning Malta as a centre of excellence and innovation in digital technologies. The island countrys Innovation Authority is responsible for establishing and enforcing relevant standards while taking appropriate measures to ensure consumer protection.

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AI and Machine Learning Technologies Are On the Rise Globally, with Governments Launching Initiatives to Support Adoption: Report - Crowdfund Insider

In machine learning, while building a classification model we sometimes come to situations where we do not have an equal proportion of classes. That means when we have class imbalance issues for example we have 500 records of 0 class and only 200 records of 1 class. This is called a class imbalance. All machine learning models are designed in such a way that they should attain maximum accuracy but in these types of situations, the model gets biased towards the majority class and will, at last, reflect on precision and recall. So how to build a model on these types of data set in a manner that the model should correctly classify the respective class and does not get biased.

To get rid of these imbalance class issues few techniques are used called as Imblearn Technique that is mainly used in these types of situations. Imblearn techniques help to either upsample the minority class or downsample the majority class to match the equal proportion. Through this article, we will discuss imblearn techniques and how we can use them to do upsampling and downsampling. For this experiment, we are using Pima Indian Diabetes data since it is an imbalance class data set. The data is available on Kaggle for downloading.

What we will learn from this article?

Class imbalance issues are the problem when we do not have equal ratios of different classes. Consider an example if we had to build a machine learning model that will predict whether a loan applicant will default or not. The data set has 500 rows of data points for the default class but for non-default we are only given 200 rows of data points. When we will build the model it is obvious that it would be biased towards the default class because its the majority class. The model will learn how to classify default classes in a more good manner as compared to the default. This will not be called as a good predictive model. So, to resolve this problem we make use of some techniques that are called Imblearn Techniques. They help us to either reduce the majority class as default to the same ratio as non-default or vice versa.

Imblearn techniques are the methods by which we can generate a data set that has an equal ratio of classes. The predictive model built on this type of data set would be able to generalize well. We mainly have two options to treat an imbalanced data set that are Upsampling and Downsampling. Upsampling is the way where we generate synthetic data so for the minority class to match the ratio with the majority class whereas in downsampling we reduce the majority class data points to match it to the minority class.

Now lets us practically understand how upsampling and downsampling is done. We will first install the imblearn package then import all the required libraries and the pima data set. Use the below code for the same.

As we checked there are a total of 500 rows that falls under 0 class and 268 rows that are present in 1 class. This results in an imbalance data set where the majority of the data points lie in 0 class. Now we have two options either use upsampling or downsampling. We will do both and will check the results. We will first divide the data into features and target X and y respectively. Then we will divide the data set into training and testing sets. Use the below code for the same.

X = df.values[:,0:7]

y = df.values[:,8]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=7)

Now we will check the count of both the classes in the training data and will use upsampling to generate new data points for minority classes. Use the below code to do the same.

print("Count of 1 class in training set before upsampling :" ,(sum(y_train==1)))

print("Count of 0 class in training set before upsampling :",format(sum(y_train==0)))

We are using Smote techniques from imblearn to do upsampling. It generates data points based on the K-nearest neighbor algorithm. We have defined k = 3 whereas it can be tweaked since it is a hyperparameter. We will first generate the data point and then will compare the counts of classes after upsampling. Refer to the below code for the same.

smote = SMOTE(sampling_strategy = 1 ,k_neighbors = 3, random_state=1)

X_train_new, y_train_new = smote.fit_sample(X_train, y_train.ravel())

print("Count of 1 class in training set after upsampling :" ,(sum(y_train_new==1)))

print("Count of 0 class in training set after upsampling :",(sum(y_train_new==0)))

Now the classes are balanced. Now we will build a model using random forest on the original data and then the new data. Use the below code for the same.

Now we will downsample the majority class and we will randomly delete the records from the original data to match the minority class. Use the below code for the same.

random = np.random.choice( Non_diabetic_indices, Non_diabetic 200 , replace=False)

down_sample_indices = np.concatenate([Diabetic_indices,random])

Now we will again divide the data set and will again build the model. Use the below code for the same.

Conclusion

In this article, we discussed how we can pre-process the imbalanced class data set before building predictive models. We explored Imblearn techniques and used the SMOTE method to generate synthetic data. We first did up sampling and then performed down sampling. There are again more methods present in imblean techniques like Tomek links and Cluster centroid that also can be used for the same problem. You can check the official documentation here.

Also check this article Complete Tutorial on Tkinter To Deploy Machine Learning Model that will help you to deploy machine learning models.

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What is Imblearn Technique - Everything To Know For Class Imbalance Issues In Machine Learning - Analytics India Magazine