PRO Newsletter Sneak Peak - AI Frontiers

AI Frontiers: Innovating Health, Business & Beyond | Business Analytics Review PRO

May 11, 2025

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Hi and welcome to today’s PRO Edition of Business Analytics Review!

We’re diving into scientists harnessing AI-driven facial analysis for predicting cancer outcomes and OpenAI’s ambitious plan to expand its Stargate project, alongside France’s Mistral and Salesforce spearheading innovative AI moves in business and beyond. Today’s edition also features an inspiring prompt for IBM’s watsonx.data, a hands-on Python code snippet for sentiment analysis, an exclusive NVIDIA AI Podcast episode on enterprise AI infrastructure, insightful industry reports from Accenture, BCG, McKinsey, and KPMG, alongside the UAE’s bold AI education initiative, and a must-attend global AI Expo in Dubai. Let’s dive in!

E-book of the Week

By Andreas Kretz

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Latest Trends/News in AI/Data Science/Business Analytics

Scientists use AI facial analysis to predict cancer survival outcomes
Scientists are using AI-driven facial analysis to help predict cancer patients’ survival outcomes, offering a non-invasive tool that could improve prognosis accuracy and personalize treatment plans

OpenAI plans Stargate expansion outside US
OpenAI is planning to expand its Stargate artificial intelligence data center project outside the US, aiming to boost global AI infrastructure and meet growing international demand for advanced AI capabilities

France’s Mistral Releases AI Model Tailored for Businesses
France’s Mistral has launched “Mistral Medium,” an AI model tailored for businesses, enabling private deployment on company infrastructure and addressing Europe’s concerns over reliance on Silicon Valley technology

Salesforce Kick Starts $500 Million Saudi AI Push With New Hire
Salesforce is investing $500 million over five years to boost AI adoption in Saudi Arabia, opening a Riyadh headquarters, hiring up to 40 staff, and appointing Ibrahim Alseghayr as regional VP

Prompt of the Day

You are an AI advisor to a mid-sized financial services company looking to implement IBM's new watsonx.data capabilities. Create a detailed implementation roadmap that identifies the company's current data silos, outlines specific steps for data integration, and proposes a timeline for deploying watsonx.data intelligence to extract insights from unstructured client communications. Include key stakeholders, potential challenges, and metrics for measuring implementation success.

Code Block: Fine-tuning a pre-trained language model for sentiment analysis

import pandas as pd
from sklearn.model_selection import train_test_split
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
import torch

# Prepare a sample dataset
data = pd.DataFrame({
    'text': ["Excellent product", "Poor customer service", "Great value", "Not satisfied"],
    'label': [1, 0, 1, 0]
})
train_texts, val_texts, train_labels, val_labels = train_test_split(
    data['text'].tolist(), data['label'].tolist(), test_size=0.25, random_state=42
)

# Load tokenizer and model from Hugging Face
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

def tokenize_function(texts):
    return tokenizer(texts, padding="max_length", truncation=True)

# Tokenize the data
train_encodings = tokenize_function(train_texts)
val_encodings = tokenize_function(val_texts)

# Create a PyTorch dataset
class SentimentDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels
    def __len__(self):
        return len(self.labels)
    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item

train_dataset = SentimentDataset(train_encodings, train_labels)
val_dataset = SentimentDataset(val_encodings, val_labels)

# Set up training arguments
training_args = TrainingArguments(
    output_dir='./results',
    num_train_epochs=2,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    evaluation_strategy="epoch",
    logging_dir='./logs',
    logging_steps=10,
)

# Instantiate the Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
)

# Train and evaluate the model
trainer.train()
trainer.evaluate()

Relevant Reports by Global Organizations/Businesses on AI

The Front-Runners’ Guide to Scaling AI
The report by Accenture analyzes how leading global companies successfully scale AI for enterprise reinvention. Front-runners integrate “agentic” AI deeply, make strategic bets, and build strong data foundations. Only 8% are truly reinvention-ready, excelling in new AI capabilities. These leaders expect rapid gains in productivity, revenue, and efficiency, highlighting five imperatives for scaling AI successfully

Artificial Intelligence Stakes a Claim on Medtech
The report by BCG details the rapid growth of AI/ML in medical technology, with FDA authorizations soaring 35-fold since 2015. Radiology leads adoption, but other specialties are catching up. Most devices are software-based. Venture capital investment has reached $14 billion, with private companies and innovation hotspots driving the sector’s mainstream acceptance

The State of AI: How organizations are rewiring to capture value
The report by McKinsey & Company finds organizations are rapidly adopting generative AI, with over 75% using AI in at least one function. Large companies lead, redesigning workflows, elevating governance (often with CEO oversight), and mitigating risks like inaccuracy and cybersecurity. It states success requires top-down transformation, centralized risk management, and ambitious, enterprise-wide AI strategies for real business impact

Trust, attitudes and use of artificial intelligence: A global study 2025
The report by KPMG and University of Melbourne is a global study of 48,000 people across 47 countries. It finds 54% are wary of trusting AI, though 72% accept its use. Regular AI use is higher in emerging economies, with younger, educated, and trained individuals reporting more trust and literacy. Concerns include cybersecurity, misinformation, and job loss. Strong public demand exists for robust AI regulation

Latest Government Initiatives to Increase AI Development

UAE Rolls Out AI for Schoolkids in New Push for Sector Forefront
The United Arab Emirates is set to introduce artificial intelligence into its public school curriculum starting in the 2025-2026 academic year, covering students from kindergarten through 12th grade. This initiative aims to position the UAE as a regional leader in AI development. The curriculum will teach foundational AI concepts, real-world applications, and ethical considerations, reflecting the country’s commitment to future-proofing its education system and workforce for the digital age

AI-Related Global Event of Significant Importance

World AI Technology Expo | Dubai, UAE | 14-15th May 2025
World AI Expo Dubai 2025 is the world’s largest AI conference, featuring workshops, hackathons, awards, and seminars. Over 1,500 global AI leaders attend, offering unmatched networking, insights, and innovation opportunities

We hope this edition of Business Analytics Review PRO provides valuable insights into the rapidly evolving AI landscape. Stay tuned for our next issue, where we'll continue tracking the latest developments in business analytics and artificial intelligence.

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