About

What we do?

 

Cortex Logic is a machine intelligence software & solutions company that provides an AI Engine for Platform Businesses and Corporates and solves well-defined and operationally relevant problems through operationalizing Data Science and Big Data & Analytics and delivering state-of-the-art Machine Intelligence based applications, solutions and products such as intelligent virtual assistants and advisors, fraud detection, churn prediction, smart risk scoring, smart trading, real-time customer insights, smart recommendations and purchase prediction, and smart payment for finance, healthcare, education, retail, telecoms, industrial and public sector as well as other industries where the automation of tasks can lead to economic benefit, scalability and productivity. Cortex Logic helps businesses and organizations thrive by unlocking the value from their data, all in support of its mission to help shape a better future in the Smart Technology Era. It also aims to contribute towards solving intelligence through advancing the state-of-the-art in machine intelligence and building cognitive systems that are contextually aware, learn at scale, support unsupervised learning where possible, reason with purpose and interact with humans naturally

 

Cortex Logic (Pty) Ltd was founded in 2015 by Dr Jacques Ludik, a smart technology entrepreneur, machine intelligence expert, investor, and ecosystem builder who has also founded the Machine Intelligence Institute of Africa (MIIA) and Bennit.AI, amongst other machine /artificial intelligence related companies (e.g., CSense Systems, which was sold to General Electric is 2011). Although the company is headquartered in Cape Town, South Africa, it operates in a virtual way with machine / artificial intelligence experts, data scientists, developers and out-of-the-box thinkers around the globe.

Machine Intelligence Solutions

Cortex Logic makes Machine Intelligence work through ...

 

  • building cognitive applications such as intelligent virtual assistants and robo-advisors for individual and business use to enhance productivity, fast track and extend human expertise, enhance discovery and exploration, improve predictions and decision making, save costs, increase revenue, reduce risks, increase asset reliability and optimize operations; Some examples include personal financial assistants, robo-advisors for insurance plan selection, portfolio management, and wealth management, personal shopping bots, customer support bots, security bots, travel agents, personal health assistants/advisors, personal knowledge assistants/advisors, tutors and learning partners, and personal agricultural assistants/advisors;
  • delivering state-of-the-art Machine Intelligence based solutions in various industries such as finance, education, healthcare, telecommunications, media, retail, transportation, law, resources, utilities, and mining as well as the public sector. Besides intelligent virtual assistants and robo-advisors other solutions include real-time fraud detection, smart churn prediction, risk scoring, recommendation engines, purchase prediction, smart trading, real-time consumer insights and behavior analysis, social network analysis, process and equipment performance enhancement, and smart payment;
  • utilizing a cognitive computing application stack consists of natural language processing of structured, unstructured, streaming in big data or smart data layers with machine learning for reasoning and learning to generate contextual patterns and associations that enable humans to connect the dots faster and smarter for more informed decisions to drive better outcomes (see Cognitive Computing section for more details);
  • integrating Machine Intelligence, Internet of Things, and Blockchain technologies where needed in a cost-effective manner (see Cognitive Computing section for more details);
  • advancing the state of the-art in Machine Intelligence to solve intelligence for a better world (see Research and Development (R&D) section for more details).

 

 

Cortex Logic solutions are typically characterized by:

  • Solve core business/customer problems/needs in a practical, cost-effective way using all available data & smart technology
  • Use state-of-the-art Machine Intelligence where appropriate
  • Being end-to-end, full stack, integrated, scalable, and secure
  • Automated analytics within champion-challenger approach

 

Cortex Logic delivers Intelligent Virtual Assistants and Advisors

 

The spectrum of intelligent virtual assistants and advisors starts from those with low technology integration and complexity such as chatbots that simply provide information, to ones that are more integrated and complex and can provide assistants and even advice. Some examples include personal financial assistants, robo-advisors for insurance plan selection, portfolio management, and wealth management, personal shopping bots, customer support bots, security bots, travel agents, personal health assistants/advisors, personal knowledge assistants/advisors, tutors and learning partners, and personal agricultural assistants/advisors.

 

Intelligent virtual assistants & robo-advisors and automation of certain tasks:

  • financial services (wealth management, insurance (e.g., insurance plan selection), portfolio management, personal financial assistant, etc.)
  • medicine and healthcare (medical assistants and advice, wellness, medical insurance, etc.)
  • education (tutors, knowledge assistant, learning partner, etc.)
  • retail (personal shopping, etc.)
  • call centres and customer support (e.g., customer support bots)
  • travel agents
  • security bots
  • monitoring and diagnostics
  • process supervision
  • personal administration and services

 

As illustrated in the diagram below the general complexity of deployment with respect to chatbots, intelligent virtual assistants and robo-advisors various, amongst others, with respect to the following characteristics:

  • From no or few business rules deployed to a large number of expert and/or data-driven rules utilized
  • From static information to more dynamic information
  • From no machine learning to more end-to-end use of machine learning (e.g. deep learning) making use of all available data sources
  • From just information-simple search to intuitive guidance based on keywords, ontology, and deep language understanding
  • From query and response (single instance) to response based on context, history and profile
  • From FAQs, standard operating procedures and limited knowledge bases to guidance on process, related transactions, and relevant knowledge
  • From single language - single Avatar to multi-language support - multi Avatar for each login or account.
  • From no or limited support services to more deeply integrated human expert and/or machine intelligence driven support services
  • From no or shallow integration into business, operational, and data systems to deep integration into all data sources, and/or business and operational systems
  • From no or shallow Internet of Things (IoT) integration to deep personal, home, and/or industrial IoT integration.

 

Intelligent virtual assistants and robo-advisors are, for example, a good fit for life and short term insurers.  Machine Intelligence's initial impact primarily relates to improving efficiencies and automating existing customer-facing, underwriting and claims processes. It is also clear that the impact of Machine Intelligence will be more profound as it will be used to identify, access and underwrite emerging risks and identify new revenue sources. the diagram below illustrated some of the Machine Intelligence applications for the Insurance industry with respect to enhance services such as personalized customer experience, digital advice, automating and augmenting underwriting and robo-claims adjusting. The specific value added are respectively redefining the value proposition to the customer, redefining distribution, enhancing efficiencies and reducing claims processing time and costs. Some of the Machine Intelligence capabilities include natural language processing. machine learning (including deep learning), graph analysis, deep question and answer systems, audio/speech analytics, sensors/IoT, soft robotics, virtual personal assistants and simulation modelling.

 

Cortex Logic delivers Industry-specific Machine Intelligence based solutions

 

Some example Machine Intelligence based solutions include the following:

Financial Services

  • Real-time customer insight
  • Risk analysis and management
  • Fraud detection and security analytics
  • CRM and customer loyalty programs
  • Credit risk, scoring, and analysis
  • Churn mitigation, response modeling
  • High speed Arbitrage trading
  • Trade surveillance, abnormal trading patterns, market manipulation and fraud detection
  • Smart payment systems

 

Retail/eCommerce

  • Merchandizing and market basket analysis
  • Campaign management and customer loyalty programs
  • Supply-chain management and analytics
  • Event- and behavior-based targeting
  • Market and customer segmentations
  • Recommendation engines - increase average order size by recommending complementary products based on predictive analysis for cross-selling
  • Cross-channel analytics
  • Right offer at the right time

 

Communication, Media, Education & Technology

  • Personalized learning, intelligent learning assistants and advisors, and learning analytics
  • Revenue assurance and dynamic pricing
  • Customer churn prevention
  • Real-time CDR (Call Direct Records) and IPDR (Internet Protocol Detail Records) analysis for network
  • Campaign management and customer loyalty
  • Network performance and optimization
  • Mobile user local analysis
  • Sentiment analysis, social gaming, online dating, influence, and social graph/network analysis

 

Healthcare & Life Sciences

  • Health-insurance fraud detection
  • Campaign and sales program optimization
  • Brand and reputation management
  • Patient care quality & program analysis
  • Drug discovery & development analysis
  • Real-time diagnostic data analysis
  • Research and development

 

Public Sector

  • Compliance and regulatory analysis
  • Fraud detection, threat detection, cyber-security, intrusion detection analysis, surveillance and monitoring
  • Smart cities, e-governance
  • Energy consumption and carbon footprint management
  • Smart payment systems

 

Resources / Utilities / Mining / Industrial

  • Smart grid, smart meters
  • Seismic data analysis
  • Rapid process and equipment troubleshooting
  • Real-time process and equipment monitoring, diagnostics and anomaly detection
  • Advanced process control
  • Process performance enhancement

R&D

 

Research and Development

 

Cortex Logic's research and development (R&D) efforts are focused on solving intelligence to have a positive impact on the world and help solve real-world problems. Cortex Logic is specifically developing contextually aware cognitive systems that learn at scale, support unsupervised learning, reason with purposes and interact with humans naturally. In order to this we are building a world-class team of machine / artificial intelligence and data science experts, developers and out-of-the-box thinkers. To support this we are, amongst other partners, working closely with the Machine Intelligence Institute of Africa (MIIA), an innovative community and accelerator for machine intelligence and data science research and applications. Cortex Logic intends to also collaborate with other research/academic organizations and companies that's advancing the state-of-the-art in Machine Intelligence and contribute to machine intelligence related open source projects. In order to accelerate our research and development efforts (as illustrated in the diagram below), Cortex Logic is enhancing its own proprietary software, algorithms, APIs, and services with a combination of best-of-breed open source and commercial APIs, services, libraries, tools, platforms, repositories and cloud infrastructure.

 

 

Advancing the state-of-the-art in Machine Intelligence

 

Given Cortex Logic's focus on solving intelligence and advancing the state-of-the-art in Machine Intelligence, herewith a brief list of some of the current research areas in this regard:

  • Recurrent Neural Networks & Sequence-to-Sequence Machine Learning
  • Unsupervised Learning by incorporating learnings from both neuroscience and Machine Learning (e.g., build causal understanding of sensory space with temporal correlations of concurrent & sequential sensory signals)
  • Use of Tensor methods and novel techniques in non-convex optimization to solve complex highly dimensional problems
  • Topological based analysis of data sets to uncover the shape of data sets
  • Attention models for powerful learning algorithms that require ever less data to be successful on harder problems
  • Knowledge representation architectures instantly malleable & shapeable (e.g., sensory learning using patterns & sequences of patterns in cause-effect vs probabilistic way)
  • Application of evolutionary computation in applications such as robotics, software agents, design and web commerce
  • Combining control principles with reinforcement learning for robust Machine Learning
  • Probabilistic Graphical Models

Cognitive Computing

What is Cognitive Computing?

 

Cognitive computing combines a range of Machine Intelligence technologies to hypothesize, recommend, adapt to learn from interactions, and then reason through dynamic experience just like humans. But it is not about replacing humans with machines. It’s about harnessing the combined strengths of both to solve complex problems from ever-changing factors and new information. The “programmable era” of computers invariably will be transcended by cognitive computing systems and applications.

Cognitive computing brings the ability to mimic the human brain, to learn, to understand in context and be more of an assistant than a tool. It understands (by sensing and interacting with data), reasons (generating hypotheses and recommendations) and learns (what the lessons from masses of data are). Cognitive computing is able to take knowledge from different sources, bring it together, and take advantage of large volumes of data and understand it without human involvement in every step. It is this cognitive capacity that will revolutionize computing as we know it. Cognitive computing will also power the Internet of Things, unlocks its true value and infuse intelligence into and learn from the physical world. 

 

 

Cognitive Computing Stack

 

Cognitive computing is natural language processing of structured, unstructured, streaming in big data or smart data layers with machine learning for reasoning and learning to generate contextual patterns and associations that enable humans to connect the dots faster and smarter for more informed decisions to drive better outcomes.

 

 

 

What are some of the Applications of Cognitive Computing?

 

The applications of cognitive computing to business are endless. Some experts believe that this technology represents our best — perhaps our only — chance to tackle some of the most enduring systemic issues facing our planet, from understanding climate change to identifying risk in our increasingly complex economy. The capabilities enabled by cognitive computing will force business leaders to rethink their operating models. While some processes may be refined, others will need to be reinvented, and still others built from scratch. New skills and training will be required, such as developing the ability to design and frame appropriate challenges for cognitive systems and applications. New ways of thinking, working and collaborating will invariably lead to cultural and organizational change, some of which may be challenging.

Cognitive computing systems have obvious benefits in the fields of medicine, finance, law, and education. These systems can also be applied in other areas of business including consumer behavior analysis, customer support bots, personal shopping bots, tutors, travel agents, security, and diagnostics.  Cognitive computing user interfaces are contingent on vertical use cases and targeted users, e.g., clinician, knowledge worker, and consumer, within industries such as Life Sciences, Energy, Oil & Gas, Public Sector, Financial Services, Manufacturing, Retail, Collaboration, Customer Services, etc.

Cognitive software platforms facilitates the development of intelligent, advisory, and cognitively enabled solutions. Cognitive applications typically involves text and rich media analytics, tagging, searching, machine learning, categorization, clustering, hypothesis generation, question answering, visualization, filtering, alerting, and navigation.

 

How will Cognitive Computing impact the Internet of Things?

 

Cognitive computing is essential to tapping into the full potential and promise of the Internet of Things (IoT). The purpose of the Internet of Things is to connect us more closely with the physical world and share information with us about the tools we use, the homes and buildings we live in, and the cars we drive. But without cognitive computing, the usefulness of this information would be limited by its own complexity and scale. According to the IDC, the IoT network will by 2020 consist of more than 29 billion connected devices. When cognitive computing is applied to the IoT, the result is systems that infuse intelligence into, and learn from, the physical world. This is what can be defined as the Cognitive IoT.  In addition to generating answers to numerical problems, cognitive systems can present unbiased hypotheses, reasoned arguments and recommendations. They understand an individual organization’s goals, and can integrate and analyze the relevant data to help individuals and businesses achieve those goals. Rather than being explicitly programmed, cognitive applications learn from interactions with humans and their experiences with their environment in a non-deterministic or probabilistic way. This enables cognitive solutions to keep pace with the complexity, volume, and unpredictability of information generated by the IoT. 

The Cognitive IoT enables fuller human interactions with people, fast tracking and extending of human expertise, the infusion of cognition into business processes, operations, products and services as well as enhanced discovery and exploration.

 

Implications of Blockchain technology for IoT and Cognitive Systems

 

Although blockchain* technology, which underlies cryptocurrencies such as Bitcoin, has only been explored for a few years, there are a number of important implications for the IoT, smart devices and cognitive systems.  Blockchain technology could provide a way to track the unique history of individual devices, by recording a ledger of data exchanges between it and other devices, web services, and human users. It could also enable cognitive and smart devices to become independent agents, autonomously conducting a variety of transactions. Some examples include:

  • a suite of smart home appliances that can bid with one another for priority so that the laundry machine, dishwasher and vacuum cleaner all run at an appropriate time while minimizing the cost of electricity against current grid prices
  • a vehicle that can diagnose, schedule and pay for its own maintenance
  • a vending machine that can not only monitor and report its own stock, but can solicit bids from distributors and pay for the delivery of new items automatically based on the purchase history of its customers

Blockchain networks themselves also have the potential to become independent agents, what has also been referred to as Distributed Autonomous Corporations (DACs). These DACs could effectively supplant systems like banking and arbitration, which have traditionally relied on trusted and centralized human authorities, with trustless and decentralized networks. Examples include:

  • escrow services to transfer ownership rights
  • electronic couriers to securely transfer sensitive information,
  • auto-installation services to verify and push updates to the software governing other DACs.

In order for IoT with its cognitive systems and smart devices to be safe, scalable and efficient, the IoT networks must be re-architected to gradually shift from managing billions of devices to hundreds of billions of devices (as illustrated in the figure below).

Decentralized_IoT

 

"In the absence of a centralized server brokering messages, supporting file storage and transfers, and arbitrating roles and permissions, any decentralized IoT solution should support three foundational types of transactions:

• Trustless peer-to-peer messaging
• Secure distributed data sharing
• A robust and scalable form of device coordination."

* A blockchain is a type of distributed ledger (which is a consensus of replicated, shared, and synchronized digital data geographically spread across multiple sites, countries, and/or institutions) comprised of unchangeable, digitally recorded data in packages called blocks.

 Source: IBM Institute for Business Value (PDF) & http://www.blockchaintechnologies.com/ 

 

 

Some key differentiators of Cognitive Computing Applications

 

  • Context-driven dynamic algorithms for automating pattern discovery and knowledge
  • Reasons and learns instantly and incrementally to discern context for sense-making
  • Cognitive systems infer, hypothesize, adapt, and improve over time without direct programming

 

The Role of the Machine Intelligence expert and/or Data Scientist in Cognitive Computing Applications

 

  • Rather than having data scientists creating algorithms to understand a particular business issue, cognitive analytics seeks to extract content, embed it into semantic models, discover hypotheses and interpret evidence, provide potential insights and then continuously improve on them.
  • The machine intelligence expert and/or data scientist’s job is to empower the cognitive application, providing guidance, coaching, feedback, and new inputs along the way. As the cognitive application moves closer to being able to replicate the human thought process, answers come more promptly and with greater consistency.

 

Join Us

Join Us

 

Cortex Logic is on a mission to solving intelligence through advancing the state-of-the-art in Machine Intelligence and solving real-world problems via building smart applications and providing data science services. In order to this we are building a world-class team of machine / artificial intelligence experts, data scientists, developers and out-of-the-box thinkers.  We welcome partners and like-minded people that share our passion and vision. Although we are based in Cape Town, we operate in a virtual way with people around the globe. Anyone interested to join the Cortex Logic team, can send their resume here.

 

 

 

 

 

Contact

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