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US tech firm Salesforce to invest US$1 billion in Singapore
12 Mar 2025 02:42PMUS cloud software giant Salesforce said Wednesday (Mar 12) it planned to invest US$1 billion in Singapore over the next five years to boost the use of artificial intelligence agents (AI) in the workforce.
The island state has been increasingly turning to technology to solve its workforce problem as it struggles to deal with a declining birthrate and an ageing population.
The investment aims to accelerate the adoption in Singapore and the region of Agentforce, which enables businesses to create and manage AI agents designed to independently perform tasks such as sales, customer service and marketing.
Unlike chatbots that follow a preset script, AI agents are more advanced as they can think, decide and take action and complete tasks such as booking appointments or processing requests.
"We are in an incredible new era of digital labour where every business will be transformed by autonomous agents that augment the work of humans, revolutionising productivity and enabling every company to scale without limits," Salesforce chief executive Marc Benioff said in a statement.
"Singapore is at the forefront of this shift, and as the world's largest provider of digital labour through our Agentforce platform, Salesforce is thrilled to expand our work with the business community and our longtime partners in the region to drive innovation, productivity and growth."
In an interview with CNBC, Benioff said Salesforce had been in Singapore for the past 25 years and described it as the hub of Southeast Asia.
"We're investing another billion dollars in our operations here and we don't just do sales and marketing. We also do extremely advanced artificial intelligence development," he added.
"We have dozens of ... the very best AI engineers in the world here in our Singapore research centre."
Salesforce noted that Singapore "has been dealing with a slowing growth rate of the labour force, contributed by an ageing population and declining birth rates".
In a separate joint announcement, Salesforce and Singapore Airlines said the carrier would use Agentforce to "streamline its customer service operations".
US tech firm Salesforce to invest US$1 billion in Singapore
US cloud software giant Salesforce said Wednesday (Mar 12) it planned to invest US$1 billion in Singapore over the next five years to boost the use of artificial intelligence agents (AI) in the workforce. The island state has been increasingly turning to technology to solve its workforce problem...
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The Beginner’s Guide to Language Models with Python
By Iván Palomares Carrascosa on March 11, 2025 in Language Models
Introduction
Language models — often known for the acronym LLM for Large Language Models, their large-scale version — fuel powerful AI applications like conversational chatbots, AI assistants, and other intelligent text and content generation apps. This article provides a concise and basic understanding of LLMs, followed by three code-based introductory examples to illustrate their use through several well-known frameworks like Hugging Face, Ollama, and Langchain. Don’t worry if some of these terms sound unfamiliar to you at this point: by the end of this reading, you’ll become acquainted with all of them.
What are Language Models?
At their essence, language models are natural language processing (NLP) systems capable of predicting the next word in a sequence, after having learned complex human language patterns by having been exposed to vast datasets of text data, typically thousands, millions, or even billions of documents. Of course, language models, in particular LLMs, have significantly evolved to accommodate many complex language understanding tasks beyond just next-word generation: they can answer questions, summarize or translate texts, and even extract insights or classify them.
Applications based on LLMs exist in different forms:
- API-based models like OpenAI’s GPT-4 (popularly known as “ChatGPT”) and Anthropic’s Claude are accessible worldwide via their websites or downloadable apps.
- Local models like LLaMA, Mistral, and Qwen, are normally run on personal or on-premises hardware.
- Hybrid models like Langchain enable app integration with other frameworks.
In the remainder of this article, we will stick to free and open-source models you will be able to try for free. Besides, the examples shown are configured to be run on an instance of Google Colab or Jupyter notebooks, thereby easing or even bypassing local configurations steps otherwise needed in your machine. Feel free to adapt them for their use in a Python IDE if you are acquainted with them.
Using Hugging Face’s Transformers Library
Hugging Face is a repository that provides open-source pre-trained language models ready to load and use for NLP tasks like text generation, translation, and sentiment analysis. It is powered by its centerpiece library: Transformers, which offers seamless integration with popular Python libraries like PyTorch, JAX, and TensorFlow. Best of all: they are free to use and require minimal setup, making AI development accessible to everyone.
Let’s start this practical tour by installing the transformers library in a new notebook of your own:
We will now load a model from Hugging Face, specifically GPT-2 for text generation. When loading pre-trained models, we normally need to load not only the model itself, but also a compatible tokenizer responsible for splitting text inputs into logical language units called tokens (roughly equivalent to words in most cases) before being passed to the language model for processing it and generating follow-up text.
The following code imports the necessary packages and initializes the language model and tokenizer.
Next we define a prompt for the model, which is typically a query, question, or request to which the language model will try to generate a response. Some less sophisticated models limit themselves to generating follow-up words that continue the input sequence or prompt, for instance to continue a tale that starts with “Once upon a time”. In our case, we will try asking GPT-2 what is the capital of Japan and … fingers crossed.
Getting technical, in the above code you may have noticed that there are two processing steps taking place: encoding and decoding. The input sequence (the prompt) must be encoded into a numerical vector representation understandable by the model, and after it generates the raw (numerical) response, it is decoded back into text to make it understandable by us.
This is the decoded response:
Well, at least it gave the right answer! These pre-trained models are comparatively smaller, more manageable for testing environments, and not as absurdly powerful and high-performing as ChatGPT ones, hence it is not surprising they might be more limited in their text generation behavior. In particular, since we specified a maximum response length of 50 tokens, the model seems to prioritize the need to provide a response that closely matches that length, rather than keeping it short, simple, and logical.
Time to look at another example and introduce another framework.
Running Language Models Locally with Ollama
Ollama is a framework that enables running language models locally in a streamlined and efficient way. For example, one of its available models is Qwen, which is a versatile open-source LLM capable of generating human-like text.
In contrast with Hugging Face models accessible via the Transformers library, Ollama’s models allow offline inference, reducing dependency on external APIs and internet connectivity (if used locally). The downside is the significant disk space needed to download the framework in your machine. While the usual procedure to try running one of Ollama’s language models locally would involve running our Python code and doing the necessary configurations on our machine, there is a workaround to be able to emulate the process in the comfort of our Google Colab notebook.
Let’s see how.
We first install Colab-xterm, a Google Colab extension to be able to run a command line terminal inside our notebook:
Next, the following simple instruction will open a terminal inside the notebook:
The Beginner's Guide to Language Models with Python - MachineLearningMastery.com
This article provides a concise and basic understanding of LLMs, followed by three code-based introductory examples to illustrate their use through several well-known frameworks.
machinelearningmastery.com
IT sector correction: Opportunity to Trap?
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90% of coding by AI, fewer jobs for software engineers: Zoho’s Sridhar Vembu and OpenAI’s Sam Altman give techies a reality check
Last Updated: Mar 22, 2025, 12:47:00 PM IST
Synopsis
OpenAI CEO Sam Altman suggests that AI could reduce the need for software engineers in the future. Many companies already rely on AI for coding. Altman and other tech leaders predict that AI will eventually write most of the code. This may lead to fewer software engineering jobs. However, essential complexity will still require human expertise.
Artificial intelligence is transforming software development, with experts predicting a significant reduction in manual coding work. Zoho founder Sridhar Vembu believes AI will handle 90% of coding, eliminating repetitive tasks, while OpenAI CEO Sam Altman warns that this shift could lead to fewer jobs for software engineers in the long run.
AI Will Write Most of the Code, Says Zoho’s Sridhar Vembu
Zoho founder Sridhar Vembu believes artificial intelligence will take over most coding tasks. He attributes this to the repetitive nature of programming work, which AI can efficiently handle.In a post on X, Vembu wrote, “When people say ‘AI will write 90% of the code’ I readily agree because 90% of what programmers write is ‘boiler plate’.” He referred to the distinction between “essential complexity” and “accidental complexity,” a concept from the software engineering book The Mythical Man-Month.
According to Vembu, AI is effective in removing accidental complexity, but humans are still needed to handle essential complexity.
OpenAI CEO Sam Altman Predicts Fewer Jobs for Software Engineers
OpenAI CEO Sam Altman has said artificial intelligence could reduce the demand for software engineers over time. While this shift is not immediate, the direction is clear. “Each software engineer will just do much, much more for a while,” he said. “And then at some point, yeah, maybe we do need less software engineers.”AI as the New Tactical Edge
In an interview with Stratechery's Ben Thompson, Altman, 39, explained how AI is changing software development. He said the competitive advantage once came from mastering coding skills. Now, it is about mastering AI tools. “The obvious tactical thing is just get really good at using AI tools,” he said. He contrasted this with the early 2000s when the primary focus was becoming a good coder.AI Already Writing Code in Many Companies
Altman revealed that artificial intelligence is already responsible for writing a significant portion of code in many organizations. “I think in many companies, it's probably past 50% now,” he said. He also spoke about the future of “agentic coding,” where AI will take on more complex tasks. “The big thing I think will come with agentic coding, which no one's doing for real yet,” he said.Industry Leaders Share Similar Views
Altman’s views align with other industry leaders. Anthropic CEO Dario Amodei recently predicted that AI will write all software code within a year. Meta’s Mark Zuckerberg made a similar statement in January, telling Joe Rogan that AI will soon generate a significant portion of the code behind their applications.The Future of Software Engineering
As AI continues to evolve, its role in software development is expanding. While experts agree that AI will handle more coding tasks, questions remain about whether it can replace human problem-solving in essential complexity. The shift may not happen overnight, but the trajectory is set.
90% of coding by AI, fewer jobs for software engineers: Zoho’s Sridhar Vembu and OpenAI’s Sam Altman give techies a reality check
OpenAI CEO Sam Altman suggests that AI could reduce the need for software engineers in the future. Many companies already rely on AI for coding. Altman and other tech leaders predict that AI will eventually write most of the code. This may lead to fewer software engineering jobs. However...
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