Transformers represent a breakthrough in neural network design, particularly for tasks
involving language understanding and generation. Unlike traditional neural networks that process
inputs sequentially, transformers parallelize data processing, allowing them to handle sequences
of data, like sentences, more effectively. This makes them ideal for understanding context and
relationships in text, forming the backbone of the most advanced LLMs.
Applications in Business and Beyond
In business, transformers are revolutionizing natural language processing applications. From
chatbots that provide nuanced customer support to advanced analytics tools that can interpret
complex documents, their impact is widespread. They are not only enhancing existing applications
but also paving the way for innovative solutions in fields like healthcare, finance, and legal
services, where understanding and generating human-like text is invaluable.
Artificial neural networks in which the connections between neural layers are inspired by the
organisation of the animal visual cortex, the portion of the brain that processes images, well
suited for visual perception tasks.
Predictive Maintenance Visually inspect and provide superior quality
control on various products.
Product Development Identify parts and
components for revised product development.
Logistic Network & Warehouse
Optimization Provide optimized utilization of available parking spaces.
Marketing & Sales Employ dynamic pricing based on customer
characteristics.
Next Product to Buy / Personalized Offering Provide
targeted advertisements and promotions in order to improve the amount of hot leads. Implement a
next-product-to-buy algorithm at the dealership for product upgrades and
maintenance.
Predictive Service / Intervention Use cameras and sensors
to perform investigations and improve the response time of emergency services. Provide real-time
monitoring of the physical condition of the driver as an increased safety feature. Predict
severity of insurance claims.
Channel & Promotion Use image
recognition and GPS monitoring to understand customer behavior.
Smart
Capex Use pattern recognition on diverse data sources (eg, seismic data, satellite
imagery) to improve the mine site-selection process.
One of the most common types of artificial neural network. In this architecture, information
moves in only one direction, forward, from the input layer, through the “hidden” layers, to the
output layer. There are no loops in the network. The first single-neuron network was proposed in
1958 by AI pioneer Frank Rosenblatt. While the idea is not new, advances in computing power,
training algorithms, and available data led to higher levels of performance than previously
possible.
Sales & Demand Forecasting Use historical sales data for demand
forecasting to better optimize utilization. Use price forecasting and optimization to better
determine when to buy and sell. Manage inventory-related costs through better predictions of
requirements, supplier management, and identifying optimal stocking locations.
Customer Service Management Optimize call-center capacity planning by
improving call-volumes and average-handling-time prediction.
Customer Acquisition
/ Lead Generation Recommend likely leads to pursue to boost commercial effectiveness
in corporate banking.
These usually use two neural networks contesting each other in a zero-sum game framework (thus
“adversarial”). GANs can learn to mimic various distributions of data (for example text, speech,
and images) and are therefore valuable in generating test datasets when these are not readily
available.
Product Development Predict real-world results from fewer experiments to
reduce experimental R&D costs.
Risk Use advanced analytics to
detect fraud (eg, credit-card usage). Better model, detect, and prevent future defaults. Enhance
predictive power of traditional early-warning systems in order to reduce cost of credit. Debias
credit decisions (eg, credit underwriting) to improve speed and accuracy. Identify fraud, waste,
and abuse patterns in diverse clinical and operations data. Reduce fraud for underwriting of auto
insurance before a claim is filed.
Reinforcement learning solves the difficult problem of correlating immediate actions with the
delayed returns they produce. Like humans, reinforcement learning algorithms sometimes have to
wait a while to see the fruit of their decisions. They operate in a delayed return environment,
where it can be difficult to understand which action leads to which outcome over many time steps.
Logistics Network & Warehouse Optimization Optimize inbound and
outbound delivery network, asset utilization, and warehousing operations. Optimize supply-chain
network to minimize freight distances and costs. Use dynamic route optimization to minimize
traveling time in real time based on changed conditions (eg, traffic, weather, safety).
"Although we had an in-house Data Science team at Royal Mail, in the early
days and we didn’t yet have NLP and Deep Learning expertise. So, when we needed to analyse our customer
services audio and text data to better understand why our customers were contacting us, Balázs and his team at
Neural Machines stepped in to help us get started. They quickly developed an NLP solution and a visualisation
tool that helped us iterate quickly with our business stakeholders."
Ben Dias
Head of Advanced Analytics and Data Science @ Royal Mail
Sounds interesting?
If so, please contact us at hello@neuralmachines.co.uk!
