Category: Career Development

By 2020, Artificial Intelligence (AI) and related technologies will be found in a wide range of businesses, in a large number of software packages, and in our daily lives. AI will be one of the top five investment priorities for at least 30% of Chief Information Officers by 2020. This new gold rush has attracted global software manufacturers. Unfortunately, while the prospect of additional revenue has driven software company owners to invest in AI technology, the reality is that most companies lack the specialised personnel required to embrace AI.

An implicit point of warning in many industry surveys on AI, Machine Learning and its impact on current industries is that software developers should first focus on understanding the needs of the customer and potential benefits from AI, before chasing the hype, which has been called as “AI Washing”.

The current trust deficit in “capabilities of tech-enabled solutions” will diminish in the next ten years.

The impact of AI in the coming decade

Over the next decade, we’ll see a dramatic transition from scepticism and partial suspicion to complete reliance on AI and other advanced technology. The majority of AI-powered applications are aimed at consumers, which is another compelling reason for mainstream users to overcome their distrust over time. The Citizen Data Science community will pave the path for a new technological order by gaining more exposure and access to technological solutions for their daily activities.

While technologies like the cloud computing allow business processes to be more agile, AI and Machine Learning can influence business outcomes. People have sought to construct a machine that behaves like a person in the post-industrial period. The thinking machine is AI’s greatest gift to humanity; the arrival of this self-propelled machine has completely altered the business landscape. Self-driving cars, digital assistants, robotic factory workers, and smart cities have all demonstrated that intelligent robots are viable in recent years. AI has altered almost every industry sector, including retail, manufacturing, finance, healthcare, and media, and it is still expanding.

The Future of Machine Learning

Based on current technology and developments we can assume that all AI systems, large or small, will include some form of machine learning.As machine learning becomes more important in corporate applications, it is likely that this technology will be delivered as a Cloud-based service known as Machine Learning-as-a-Service (MLaaS).Connected AI systems will allow ML algorithms to actively learn from new emerging data in the internet.Hardware suppliers will be rushing to increase CPU power to handle ML data processing. Hardware vendors will be driven to alter their computers to better accommodate the capabilities of machine learning.

Some Predictions about Machine Learning

• Multiple Technologies in Machine Learning: In many ways, the Internet of Things has benefited Machine Learning. Variousalgorithms are now being used in machine learning to improve learning capabilities and collaborative learning using multiple algorithms is likely in the future.
• ML Developers will have access to APIs to develop and deploy “smart applications” in a personalised computing environment. This resembles “assisted programming” in someways. Developers may simply integrate facial, speech, and visual recognition features into their applications using these API kits.
• Quantum computing will dramatically improve the speed with which machine learning algorithms in high-dimensional vector processing are executed. This will be the next major breakthrough in machine learning research.
• Future advancement in “unsupervised ML algorithms” will lead to higher business outcomes.
• Tuned Recommendation Engines: In the future, ML-enabled services will be more accurate and relevant. Recommendation Engines in the future, for example, will be significantly more relevant and tailored to a user’s unique likes and tastes.

Will AI and ML impact the cyber security industry?

According to current AI and ML research trends, advances in cyber-security have advanced ML algorithms to the next level of learning, implying that future security-centric AI and ML applications will be distinguished by their speed and accuracy. Machine Learning, Artificial Intelligence, and the Future of Cyber Security contains the complete story. This emerging practise could help Data Scientists and cyber security specialists work together to achieve common software development goals.

Benefiting Humanity: AI and ML in Core Industry Sectors

It’s difficult to overlook the global impact of “AI Washing” in today’s commercial market, as well as how AI and machine learning may transform application development markets in the future.

AI and machine learning have often been compared to the discovery of electricity at the beginning of the Industrial Revolution. These cutting-edge technologies, like electricity, have ushered in a new age in information technology history.

Today, AI and machine learning-powered platforms are transforming the way businesses are conducted across all industries. These cutting-edge technologies are progressively bringing about dramatic changes in a variety of industries, including the following:

• Healthcare:Human practitioners and robots will gradually work together to improve outcomes. Smart AI enabled equipment would be expected to provide faster and accurate diagnoses of patient ailments, allowing practitioners to attend more number of patients.

• Financial Services : The article AI and Machine Learning are the New Future Technology Trends looks at how new technologies such as blockchain are affecting India’s capital markets. Capital-market operators, for example, can use blockchain to forecast market movements and detect fraud. AI technologies not only open up new business models in the financial sector, but they also strengthen the position of AI technologists in the business-investment ecosystem.

• Real Estate : Contactually.com, an innovative CRM system for the real estate industry, was created exclusively to link investors with entrepreneurs in Washington, DC. Computer Learning algorithms add to the power of the static system, transforming it into a live, interactive machine that listens, approves, and suggests.

• Administration of Databases : AI technology can automate procedures and duties in a typical DBA system because of the repeated tasks. Nowadays DBA is equipped with modern AI based algorithms so that they may make value-added contributions to their organisations rather than just executing routine jobs.

• Personal Devices : According to several analysts, AI represents a game changer for the personal device sector. By 2020, AI-enabled Cloud platforms will be used by around 60% of personal-device technology manufacturers to supply better functionality and personalised services. Artificial intelligence will provide an “emotional user experience.”

Summary: To build an efficient machine learning model for the consumer goods companies to ensure that their products are available and properly placed in stores.

The problem:

Items that are often eaten by consumers (foods, beverages, household supplies, etc.) necessitate a detailed replenishment and positioning routine at the point of sale (supermarkets, convenience stores, etc).

Researchers have frequently demonstrated over the last few years that around two-thirds of purchase choices are made after buyers enter the store. One of the most difficult tasks for consumer goods companies is to ensure that their products are available and properly placed in stores.

Teams in stores organise shelves based on marketing objectives and maintain product levels in stores. These individuals may count the number of SKUs of each brand in a store to estimate product stockpiles and market share, as well as assist in the development of marketing plans.

Preparing the data:

Gathering good data is the first step in training a decent model. As previously said, this solution will employ a dataset of SKUs in various scenarios. SKU110K was created to serve as a benchmark for models that can recognise objects in densely packed settings.

The dataset is in Pascal VOC format, which must be translated to tf.record. The conversion script can be found here, and the tf.record version of the dataset can be found in my project repository. As previously said, SKU110K is a vast and difficult dataset to work with. It has a large number of objects that are often similar, if not identical, and are arranged in close proximity.

Choosing the model:

The SKU detection problem can be solved using a number of neural networks. However, when translated to TensorFlow.js and run in real-time, the architectures that readily reach a high level of precision are quite dense and do not have tolerable inference times.

As a result, the focus here will be on optimising a mid-level neural network to attain respectable precision while working on densely packed scenes and running inferences in real-time. The task will be to tackle the problem with the lightest single-shot model available: SSD MobileNet v2 320×320, which appears to meet the criteria necessary, after analysing the TensorFlow 2.0 Detection Model Zoo. The architecture has been shown to recognise up to 90 classes and can be customised. 

Training the model:

It’s time to think about the training process now that you’ve got a decent dataset and a strong model. The Object Detection API in TensorFlow 2.0 makes it simple to build, train, and deploy object detection models. I’m going to utilise this API and a Google Colaboratory Notebook to train the model.

Setting up the environment:

Select a GPU as the hardware accelerator in a new Google Colab notebook:

Change the type of runtime > Accelerator hardware: GPU

The TensorFlow Object Detection API can be cloned, installed, and tested as follows:

Then, using the appropriate commands, download and extract the dataset:

Setting up the training pipeline

I’m now ready to set up the training pipeline. The following instructions will be used to download pre-trained weights for the SSD MobileNet v2 320×320 on the COCO 2017 Dataset from TensorFlow 2.0:

The downloaded weights were pre-trained on the COCO 2017 Dataset, but as the goal is to train the model to recognise only one class, these weights will only be used to establish the network — this technique is known as transfer learning, and it’s widely used to speed up the learning process.

Finally, on the configuration file that will be utilised throughout the training, set up the hyper parameters. Choosing the best hyper parameters is a task that necessitates some trial and error.

I used a typical setup of MobileNetV2 parameters from the TensorFlow Models Config Repository and ran a series of tests on the SKU110K dataset to optimise the model for tightly packed scenes (thanks Google Developers for the free materials). Use the code below to download the configuration and verify the parameters.

To identify how well the training is going, I am using the loss value. Loss is a number indicating how bad the model’s prediction was on the training samples. If the model’s prediction is perfect, the loss is zero; otherwise, the loss is greater. The goal of training a model is to find a set of weights and biases that have low loss, on average, across all. The training process was monitored through Tensor board and took around 22h to finish on a 60GB machine using an NVIDIA Tesla P4. 

Validate the model:

Now let’s evaluate the trained model using the test data:

The evaluation was done across 2740 images and provides three metrics based on the COCO detection evaluation metrics: precision, recall, and loss. The same metrics are available via Tensor board and can be analysed in an easier way. You can then explore all training and evaluation metrics.

Exporting the model:

It’s time to export the model now that the training has been validated. The training checkpoints will be converted to a protobuf (pb) file. This file is going to have the graph definition and the weights of the model.

As we’re going to deploy the model using TensorFlow.js and Google Colab has a maximum lifetime limit of 12 hours, let’s download the trained weights and save them locally. When running the command files. Download (“/content/saved_model.zip”), the Colab will prompt the file download automatically.

Deploying the model:

The model will be distributed in such a way that anyone with a web browser can open a PC or mobile camera and execute real-time inference. To do so, I’ll convert the stored model to TensorFlow.js layers format, load it into a JavaScript application, and make everything publicly available.

Converting the model:

Let’s start by setting up an isolated Python environment so that I may work in an empty workspace and avoid any library conflicts. Install virtualenv, then create and activate a new virtual environment in the inference-graph folder using a terminal:

venv source venv/bin/activate virtualenv -p python3

Install the TensorFlow.js converter by running pip install tensorflow.js tensorflow.js ten [wizard] install tensorflowjs

Start the conversion wizard: tensorflowjs_wizard

Now, the tool will guide you through the conversion, providing explanations for each choice you need to make. The image below shows all the choices that were made to convert the model. Most of them are the standard ones, but options like the shard sizes and compression can be changed according to your needs.

To enable the browser to cache the weights automatically, it’s recommended to split them. 

Conclusion:

Apart from the precision, one of the most intriguing aspects of these tests is the inference time – everything is done in real time in the browser using JavaScript. In many consumers packaged goods industry applications, as well as other industries, SKU identification models that run in the browser, even offline, and use low computational resources are a necessary.

Enabling a Machine Learning solution to operate on the client side is a critical step in ensuring that models are used efficiently at the point of interaction with minimal latency and that problems are solved when they occur: right in the user’s hands.

Deep learning should not be expensive and should be utilised for more than just research, with JavaScript being ideal for production deployments. I hope you find this post useful.

The reputation and success of any educational institution including higher educational institutions lies on its students – past as well as present. Faculty, administration staff and placement personnel also play a vital role. The reputation grows when it keeps producing successful graduates and post-graduates continuously – intelligent, innovative and effective in their fields.

Alumni can help institutions sustain through their donations, and voluntary help. If a big chunk of the money that institutions require come from alumni it will help those institutions remain competitive. They can provide their students newer technology, better facility, and nicer campus. Let us now study and review the possible areas in which alumni can provide a strong support to educational institutions:

Brand building

With mushrooming educational institutions around the world, one way for institutes to stand apart from domestic and international competition is to promote its brand. And an institute’s alumni are its best bet. It is not only the alumni’s professional successes that drive a brand but also factors like alumni driven campus recruitment, social campaigns go a long way in promoting a brand.

Grading

Year after year, the race for ranking has gained more prominence as more and more institutions have entered the fray. Apart from factors like infrastructure, quality of faculty, curriculum, research facilities, now alumni success and engagement are also being taken into consideration.

National Assessment and Accreditation council’s (NAAC) Revised Assessment and Accreditation framework indicates a shift in several metrics – one of it being to bring in enhanced participation of students and alumni in the assessment process. NAAC has specified that alumni engagement can contribute in academic matters, student support as well as mobilisation of resources – both financial and non-financial.

Naming rights

With the intention of encouraging significant contributors’/donors, higher educational institutions have embarked on a policy of granting naming rights to major facilities and programs.

Programs in this connection refer to scholarship, fellowship, chair-professorship, endowment, etc

Facilities in this connection refer to movable and immovable properties, research lab equipment, etc.

The name of the person, in respect of whom the Naming Rights have been granted, shall be prominently displayed at all times on the immovable or movable property during the tenure of the Naming Rights.

Placements

The alumni network of a college/university is one of the biggest sources of placement opportunities for students. Alumni can help students get placed at their respective organisations.

Mentorship and career guidance

Alumni can play an active role in voluntary programmes like mentoring students in their area of expertise. Further alumni are a huge talent pool whose guidance can be beneficial to many students in their respective areas of study.

Networking platform

Alumni network by itself is one of the biggest professional networking platforms available today.

So, it may be concluded that former students or alumni of an institution can play an immensely positive role in higher education transformation. The alumni have the great potential to promote institutional development. It has equally great ability to build a skill and knowledge-sharing network. It is therefore strategic for institutions of higher learning to establish and maintain good relations with its alumni by inviting them in decision making, network building and developmental processes for the overall advancement of the institution.