Ideas to Execution for

Pragmatic AI projects!!

Zeblok Build Intelligence Services

Overview:

Gartner forecasts AI-related services business is expected to be $350 billion by 2022, growing at a 38% CGAR.  Internet of things, 5G, artificial intelligence, machine learning, predictive analytics and many more technologies are changing the way enterprise organizations can address complex problems.

Value creation in the new AI-driven ecosystem begins by capturing a real industrial problem. Data can be in the form of videos, text, web pages, audio recordings, emails and many other formats. Delivering a pragmatic AI solution that has conceivable ROI requires solution architecture that takes into consideration data integration services, selecting the right algorithms and creating intelligent displays that are user friendly to provide answers and actionable insights.

  

Zeblok’s Build-Intelligence services provides idea creation, solution architecture development, building technical and commercial proposals, bidding for RFP’s, project planning and execution strategies. 

Use Cases

Biotech/Pharma

The Internet of Things (IoT) in smart cities market is projected to grow from USD 79.5 billion in 2018 to USD 219.6 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 22.5% from 2018 to 2023. The major factors driving the growth of the IoT in smart cities market are the increasing number of government initiatives and PPP models for smart cities, improvements in the communication infrastructure brought on by IoT, and rising adoption of connected and smart technologies in smart cities initiatives.

 

Among the solutions, remote monitoring is projected to lead the market with respect to market size during the forecast period. The major factor attributing to the segment growth is the rising adoption of connected and smart technologies involved in the implementation of IoT in smart cities solutions. These solutions help in security and monitor the assets from a remote location without physical presence. This is helping governments deliver efficient services to its citizen and also secure assets remotely.

Fintech

AI Applications

Smart Cities

The Internet of Things (IoT) in smart cities market is projected to grow from USD 79.5 billion in 2018 to USD 219.6 billion by 2023, at a Compound Annual Growth Rate (CAGR) of 22.5% from 2018 to 2023. The major factors driving the growth of the IoT in smart cities market are the increasing number of government initiatives and PPP models for smart cities, improvements in the communication infrastructure brought on by IoT, and rising adoption of connected and smart technologies in smart cities initiatives.

 

Among the solutions, remote monitoring is projected to lead the market with respect to market size during the forecast period. The major factor attributing to the segment growth is the rising adoption of connected and smart technologies involved in the implementation of IoT in smart cities solutions. These solutions help in security and monitor the assets from a remote location without physical presence. This is helping governments deliver efficient services to its citizen and also secure assets remotely.

Precision Manufacturing

Predictive maintenance and machinery inspection to hold largest size of AI in manufacturing market. The predictive maintenance and machinery inspection is a regular and systematic application of AI, which ensures proper functioning of equipment and reduces its rate of deterioration. The artificial Intelligence technology in predictive maintenance and machinery inspection encompasses regular examination, inspection, lubrication, testing, and adjustments of equipment without prior knowledge of equipment failure.

Smart Healthcare

Rising need for improvised healthcare services due to imbalance between health workforce and patients. Maintaining a balance between health workforce and patients is a challenge in developed and developing countries. AI and cognitive mobility platforms are helping medical practitioners easily and efficiently achieve their tasks with minimal human intervention. The deep learning technology in medical imaging solutions helps in various pathology tests, such as blood test, X-ray analysis, and cancer cell detection. AI technologies offer doctors with tools that help them in better diagnose and effectively treat patients.

Smart Logistics

Cybersecurity is a critical area of research as it addresses society’s increasing reliance on the connections between the physical world, people, hardware, software, networks, data, privacy and ethics. As these interconnections increase, so do vulnerabilities, which can expose the fragile nature of these systems and if exploited, cause irreparable damage

 

Typical areas of interest would be:

•  Risk and mitigation related to the field of Artificial Intelligence with potential emphasis on defending trained models against model  inversion, membership inference and developing countermeasures against techniques that may exploit supervised and unsupervised learning models.

 

•  Adoption of advanced techniques to enable warning and prediction of significant cyber events before they occur, leveraging predictive analytics, AI, ML and inputs or sensors from not typically associated with the cyber domain

 

•  Multi-dimensional threat-detection leveraging combinations of AI techniques

 

•  Leveraging advanced cryptography for Privacy Preservation while deploying AI techniques across multiple parties for cognitive decision-making use case

Drug Development

Overview:

Gartner forecasts AI-related services to reach $350 billion by 2022, growing at a 38% CGAR.  Internet of things, 5G, artificial intelligence, machine learning, predictive analytics and many more technologies are changing the way enterprise organizations can address complex problems.

Value creation in the new AI-driven ecosystem begins by addressing a real industrial problem. Data can be in the form of video, text, web pages, audio recordings, emails and many other formats. Delivering a pragmatic AI solution that has conceivable ROI requires solution architecture that takes into consideration data integration services, selecting the right algorithms and creating intelligent displays that are user friendly to provide answers and actionable insights.

  

Zeblok’s Build Intelligence service provides idea creation, solution architecture development, building technical and commercial proposals, bidding on RFPs, project planning and execution strategies. 

Use Cases

Biotech/Pharma

When it comes to drugs and medical treatments there is no "one-size-fits-all." Patients vary greatly in their needs and responses. A treatment that is life-saving for one person might be ineffective or even harmful to another. This realization has led to the revolution of personalized medicine and drug design. It is helpful here that there is a certain degree of commonality among people. Much can be learned from partitioning the overall population of patients into subpopulations that share certain common features and attributes. However, identifying well-defined subpopulations remains to be a challenging endeavor. While there is nowadays no shortage in data that can be used to minutely characterize individual patients and the symptoms they exhibit, these detailed characterizations lead to vast and unwieldy feature spaces where patient subpopulations are rarely homogeneous and often difficult to separate. Furthermore, many of the features and attributes may not be important for a particular task but that is difficult to assess beforehand. Practitioners often face with the problem of selecting the right features for analysis of a specific problem. Unable to cope, feature selection is too frequently reduced to a guessing game.

 

There are many application areas in the Biotech/Pharma sector where these critical challenges occur:

  • Rare diseases: select the most promising treatment for a given patient

  • Treatment prognosis: match drug interventions with individual patients

  • Drug testing and validation: select the most appropriate candidates for a clinical trial

  • Drug re-purposing: find new associations of disease progression patterns for a given drug

FinTech

The ubiquitous collection and availability of big data offers tremendous opportunities for the financial sector. It allows highly personalized assessments of risks and prospects, which companies can then act upon to generate substantial profits. However, raw financial data are typically rather noisy, with low veracity.  However, to overlook or misinterpret even small nuances and trends in these data can be extremely costly. Designing sensitive predictive metrics from these data also requires substantial engineering, which ups the game even further.

The goal of financial analytics is predictive and better even, prescriptive – recommendations on actions to mitigate risk and maximize profits. Both must be grounded in exquisite, ideally superior, knowledge of the domain at hand. Formalizing this knowledge from data is the job of descriptive analytics.

A key to reliable descriptive data modeling is to identifying subpopulations in the data that share certain common features, and doing so in a statistically robust manner. No sane financial strategy should be based on learning predictive models from outlier data points. The challenge is to find these stable data regions. 

One problem in this mission is the noisy nature of the data; another is the massive number of data attributes and the predictive metrics derived from them. Both lead to vast and unwieldy feature spaces where data are rarely homogeneous and subpopulations are difficult to separate.  

Furthermore, many of the features and attributes may not be important for a particular task but this is difficult to assess beforehand. Data analysts are often confronted with the problem of selecting the right features for a specific analysis problem. Unable to cope, feature selection can be reduced to a guessing game. 

There are many FinTech application areas and many other business sectors with these critical challenges:     

  • Stock market prediction: predict stock prices from past data patterns and current observations   

  • Fraud detection and prevention: identify malicious entities that try to hide, but still have common patterns  

  • Credit scoring: assess credit worthiness by estimating default risk based on past data

  • Risk management: use past internal and external data to estimate risk and prevent future losses

  • Personalized marketing: estimate a person’s subpopulation to suggest purchases or investments 

AI Applications

Drug Discovery

Global artificial intelligence in the drug discovery market is exploding at a CAGR of 40.8% and is projected to reach $1.4 billion by 2024 from $259  million in 2019. Factors such as the growing number of cross-industry collaborations and partnerships, the increasing need to control drug discovery and development costs and reduce the overall time taken in this process, the rising adoption of cloud-based applications and services, and the impending patent expiry of blockbuster drugs are the primary drivers of AI growth in drug discovery.

Artificial intelligence in the drug discovery market is segmented into software and services. In 2018, the software segment accounted for a larger market share during the forecast period. Factors such as lowering cost and time to market drugs, lowering failure rates, increasing numbers of software developers focused on drug discovery, and strong demand for the software among big pharmaceutical & biotech companies and research institutes are driving software segment growth.

Computer modeling in drug discovery: Computer modeling is often a front-line design tool. Computational modeling helps discover drugs faster and of higher quality. To use it, you need to know the protein structure, i.e. where all the atoms are located on the viral protein you want to attack. With modern technologies like cryo-EM, protein structures can be determined very quickly.

Physics-based computer modeling: Rather than empirical methods, the state-of-the-art is now computationally-intense physics-based molecular simulations. This modeling is not just large-scale filtering. It has two advantages: First, it can help medicinal chemists understand the biological mechanism of how the virus is causing damage in the first place. Second, in principle it can pinpoint more precisely which drug candidates might be the very best ones. It does this by computing the drug binding affinity to the protein target. The problems with physical modeling, however, are that: (i) they are extraordinarily expensive in GPU supercomputing time, and (ii) that they require deep expertise.

Smart Cities

The Internet of Things (IoT) in smart cities market is projected to grow from $79.5 billion in 2018 to $219.6 billion by 2023 (a CAGR of 22.5%). The major factors driving the growth of the IoT in smart cities market are the increasing number of government initiatives and PPP models for smart cities, improvements in communications infrastructure brought on by IoT and rising adoption of connected and smart technologies in smart cities initiatives.

 

Among the solutions, remote monitoring is projected to lead the market with respect to market size during the forecast period. The major factor attributing to the segment growth is the rising adoption of connected and smart technologies involved in the implementation of IoT in smart cities solutions. These solutions help in security and monitor the assets from a remote location without physical presence. This is helping governments deliver efficient services to its citizen and also secure assets remotely.

Smart Healthcare

Healthcare is extremely expensive in the United States. According to CMS, US healthcare expenditures in 2016 were $3.3 trillion, which amounts to $10,438 per person. A significant portion (i.e. 32% of this amount or $1.1 trillion) was spent on hospital procedures. Hospitals are looking to increase automation in hospital operations, while improving quality. A system can be developed to automatically detect and visualize techniques to understand stress and activity statistics of various hospital divisions and operations using data from wearable devices. 

Automated methods to detect stress (and potentially burnout) offer radical improvements over survey-based methods, especially in hospitals. Such projects provide knowledge and tools that (a) improves employees' well-being, (b) offers cost-effective and reliable methods to measure and detect stress, leading to improved employee retention and patient satisfaction in hospitals.

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