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While buzz about artificial intelligence (AI) has largely focused on the growing popularity of generative AI tools such as ChatGPT, the demand for jobs and growth in the sector is booming. In fact, AI and machine learning specialist roles are growing faster than any other occupation in the world, according to the World Economic Reports Future of Jobs Report.

Ryan Aytay, CEO of Tableau, says AI and big datas rapid growth in popularity and growth has created a need for everyone to learn the appropriate skills as well as to more broadly adopt a philosophy of lifelong learning.

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[AI] only seems to have accelerated this need for everyone, not just business users, not just analysts, really everyone to have the ability to not only see and understand but also use that data to make decisions with regardless of what they need to be focused on, Aytay says.

Over the past few months, more universities have sought to meet the AI demand head on by creating degree programs specifically focused on the subject. For example, just in March 2024, Purdue Universitya school known for its strong engineering armannounced a brand new online masters in AI.

If AI from a business perspective interests you, youre in luck, too. Many business schools now offer MBA specializations in AI as well as certifications focused on the subject.

And while there are also options to take free online courses in artificial intelligence, many schools now offer full-fledged degree tracks. Fortune compiled a list of six masters in AI programs to check out if youre looking to make a career switch.

At Duke University, students in the artificial intelligence for product innovation master of engineering program can complete courses in -person in 12 to 16 months or online within 24 months. Students can also choose from a variety of learning tracksor a focusincluding data science and machine learning.

The program also includes a capstone project and summer internship. Graduates often move intotake jobs as machine learning engineers, AI engineers, data scientists, and data engineers for companies including OpenAI, Doordash, and Targets AI Lab within six months of graduation. All students must complete an online data science and Python bootcamp the summer before the start of their program.

Students complete 10 courses during the program, covering topics including AI, machine learning, operations, and management. The management courses are offered through Dukes Law School and Fuqua School of Business, which Fortune ranks as having one of the top full-time MBA programs in the U.S.

Applicants are expected to have an undergraduate degree in science or engineering (or equivalent technical work experience), minimum one year of programming experience, two semesters completed of calculus, and meet English proficiency admission requirements (for international students).

The cost of Dukes program depends on the modality (online or in-person) and the amount of time taken to complete the degree. Applications require transcripts, short-answer essay responses, a resume, three letters of recommendation, and an introductory video. Prospective students have the option to submit GRE scores.

Format: Online or in-person

Cost: $99,734 (online); $113,892 (in-person)

Deadlines: Round 1: January 15 (online and in-person); Round 2: March 15 (in-person), April 15 (online)

Johns Hopkins University offers both a masters degree and a graduate certificate in artificial intelligence through its Whiting School of Engineering. The online masters in AI includes 10 coursesfour core courses and six electivesand students can take up to five years to complete them.

Curriculum includes algorithms, applied machine learning, and creating AI-enabled systems. Johns Hopkins does require several prerequisite courses including calculus, programming, and linear algebra, but will offer provisional admission for students to complete the required courses prior to enrollment.

GRE scores arent required to apply, but most admitted students have at least a 3.0 undergraduate GPA.

Format: Online

Cost: $52,700 (estimated total program price)

Deadlines: Open year-round (terms begin in spring, summer, and fall)

Northwestern Universitys masters in artificial intelligence seeks to train those with a desire to become architects of intelligent systems. Through the program, students learn the psychological and design implications of AI and how business needs may be satisfied.

Students can take a traditional track or choose the MSAI+X program and combine AI with their original field of study. The program is limited to approximately 40 students per year and lasts for 15 months.

Applicants should have a bachelors in computer science or related field, and preference will be given to those with at least two years of relevant work experience.

Format: In-person

Cost: ~$110,000

Deadlines: December 15 (priority); March 15 (final)

Purdues new masters in artificial intelligence seeks to prepare students to succeed in todays increasingly tech-reliant world. Students will learn practical skills in AI and computing as well as professional skills like leadership and project management and technical skills like programming and machine learning.

Participants can choose two major tracks: AI and machine learning or AI management and policy. Admissions requirements differ depending on which major is chosen. There is no application fee to apply. While English proficiency testing is required for international students, GRE and GMAT scores are not needed.

Format: Online

Cost: ~$28,000

Deadlines: August 1 (fall); December 1 (spring); April 1 (summer)

The masters in AI at the University of MichiganDearborn teaches students the foundational theory and practice of AI. The program is very flexibility in the sense that students can choose to learn online, in-person, or hybrid, and learn either on a full- or part-time basis. Because of the latter offering, courses are hosted in the late afternoon or evening hours.

Students can focus on four different concentrations: computer vision, intelligence interaction, machine learning, or knowledge management and reasoning. Admission into the program requires students to have graduated with bachelors degree in a STEM field with a B average. Mathematics skills, such as calculus III and linear algebra, is recommended but not required.

Format: Online, in-person, or hybrid

Cost: $50,208/year (direct + indirect costs, out-of-state)

Deadlines: Rolling admission

UTAustin offers its online masters program in AI through its department of computer science and machine learning laboratory, and the degree can be completed at your own pace. The degree covers about two years worth of content. The program is offered on the online education platform, edX, an online education platform, and costs $10,000 to complete, making it one of the more affordable options.

The degree covers AI-related topics, including natural language processing, reinforcement learning, computer vision, and deep learning, which prepares graduates for A.I. jobs in engineering, research and development, product management, and consulting.

The program quickly skyrocketed in popularity, with more than 4,000 prospective students requesting more information from the university within 24 hours of its launch announcement.

Prospective students must submit an application to the Graduate School at The University of Texas at Austin as well as a statement of purpose, resume, and transcripts. Letters of recommendation and GRE scores are optional to submit.

Format: Online

Cost: $10,000 (202324 academic year)

Deadlines: Fall: April 1 (priority), May 1 (final); Spring: August 15 (priority), September 15 (final)

Yes, having a masters in AI can be very beneficial for those wanting to become AI experts. However, it is also important to keep in mind that AI is always evolving. By the time your program completes, some of the skills and best practices you initially learned could be out of date.

Yes, you will need to learn how to code if you plan to study AI in an advanced degree program. Python is generally considered to be the most relevant programming language to AI. Having skills in Java, SQL, C++, and R also couldnt hurt. Some masters in AI programs, like Duke, require students to have some programming experience as well as to enroll in a Python bootcamp.

The best degree pathway for those interested in AI truly depends on your interests.

A masters in AI will likely give you a perfect entry into careers in AI, data science, machine learning, and beyond. If you know a particular specialization in the tech space interests you more than another, that is a great place to start. Above all, keep in mind that because AI masters are new, there is no perfect path; its up to you to define it.

Check out all of Fortunes rankings of degree programs, and learn more about specific career paths.

Sydney Lake contributed to this piece.

Original post:
Looking to break into AI? These 6 schools offer master's in artificial intelligence programs - Fortune

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Machine learning identifies prognostic subtypes of the tumor microenvironment of NSCLC | Scientific Reports - Nature.com

You have likely already had the opportunity to interact with generative artificial intelligence (AI) tools (such as virtual assistants and chatbot applications) and noticed that you dont always get the answer you are looking for, and that achieving it may not be straightforward. Large language models (LLMs), the models behind the generative AI revolution, receive instructions on what to do, how to do it, and a set of expectations for their response by means of a natural language text called a prompt. The way prompts are crafted greatly impacts the results generated by the LLM. Poorly written prompts will often lead to hallucinations, sub-optimal results, and overall poor quality of the generated response, whereas good-quality prompts will steer the output of the LLM to the output we want.

In this post, we show how to build efficient prompts for your applications. We use the simplicity of Amazon Bedrock playgrounds and the state-of-the-art Anthropics Claude 3 family of models to demonstrate how you can build efficient prompts by applying simple techniques.

Prompt engineering is the process of carefully designing the prompts or instructions given to generative AI models to produce the desired outputs. Prompts act as guides that provide context and set expectations for the AI. With well-engineered prompts, developers can take advantage of LLMs to generate high-quality, relevant outputs. For instance, we use the following prompt to generate an image with the Amazon Titan Image Generation model:

An illustration of a person talking to a robot. The person looks visibly confused because he can not instruct the robot to do what he wants.

We get the following generated image.

Lets look at another example. All the examples in this post are run using Claude 3 Haiku in an Amazon Bedrock playground. Although the prompts can be run using any LLM, we discuss best practices for the Claude 3 family of models. In order to get access to the Claude 3 Haiku LLM on Amazon Bedrock, refer to Model access.

We use the following prompt:

Claude 3 Haikus response:

The request prompt is actually very ambiguous. 10 + 10 may have several valid answers; in this case, Claude 3 Haiku, using its internal knowledge, determined that 10 + 10 is 20. Lets change the prompt to get a different answer for the same question:

Claude 3 Haikus response:

The response changed accordingly by specifying that 10 + 10 is an addition. Additionally, although we didnt request it, the model also provided the result of the operation. Lets see how, through a very simple prompting technique, we can obtain an even more succinct result:

Claude 3 Haiku response:

Well-designed prompts can improve user experience by making AI responses more coherent, accurate, and useful, thereby making generative AI applications more efficient and effective.

The Claude 3 family is a set of LLMs developed by Anthropic. These models are built upon the latest advancements in natural language processing (NLP) and machine learning (ML), allowing them to understand and generate human-like text with remarkable fluency and coherence. The family is comprised of three models: Haiku, Sonnet, and Opus.

Haiku is the fastest and most cost-effective model on the market. It is a fast, compact model for near-instant responsiveness. For the vast majority of workloads, Sonnet is two times faster than Claude 2 and Claude 2.1, with higher levels of intelligence, and it strikes the ideal balance between intelligence and speedqualities especially critical for enterprise use cases. Opus is the most advanced, capable, state-of-the-art foundation model (FM) with deep reasoning, advanced math, and coding abilities, with top-level performance on highly complex tasks.

Among the key features of the models family are:

To learn more about the Claude 3 family, see Unlocking Innovation: AWS and Anthropic push the boundaries of generative AI together, Anthropics Claude 3 Sonnet foundation model is now available in Amazon Bedrock, and Anthropics Claude 3 Haiku model is now available on Amazon Bedrock.

As prompts become more complex, its important to identify its various parts. In this section, we present the components that make up a prompt and the recommended order in which they should appear:

The following is an example of a prompt that incorporates all the aforementioned elements:

In the following sections, we dive deep into Claude 3 best practices for prompt engineering.

For prompts that deal only with text, follow this set of best practices to achieve better results:

The Claude 3 family offers vision capabilities that can process images and return text outputs. Its capable of analyzing and understanding charts, graphs, technical diagrams, reports, and other visual assets. The following are best practices when working with images with Claude 3:

Consider the following example, which is an extraction of the picture a fine gathering (Author: Ian Kirck, https://en.m.wikipedia.org/wiki/File:A_fine_gathering_(8591897243).jpg).

We ask Claude 3 to count how many birds are in the image:

Claude 3 Haikus response:

In this example, we asked Claude to take some time to think and put its reasoning in an XML tag and the final answer in another. Also, we gave Claude time to think and clear instructions to pay attention to details, which helped Claude to provide the correct response.

Lets see an example with the following image:

In this case, the image itself is the prompt: Claude 3 Haikus response:

Lets look at the following example:

Prompt:

Claude 3 Haikus response:

Lets see an example. We pass to Claude the following map chart in image format (source: https://ourworldindata.org/co2-and-greenhouse-gas-emissions), then we ask about Japans greenhouse gas emissions.

Prompt:

Claude 3 Haikus response:

Lets see an example of narration with the following image (source: Sustainable Development Goals Report 2023, https://unstats.un.org/sdgs/report/2023/The-Sustainable-Development-Goals-Report-2023.pdf):

Prompt:

Claude 3 Haikus response:

In this example, we were careful to control the content of the narration. We made sure Claude didnt mention any extra information or discuss anything it wasnt completely confident about. We also made sure Claude covered all the key details and numbers presented in the slide. This is very important because the information from the narration in text format needs to be precise and accurate in order to be used to respond to questions.

Information extraction is the process of automating the retrieval of specific information related to a specific topic from a collection of texts or documents. LLMs can extract information regarding attributes given a context and a schema. The kinds of documents that can be better analyzed with LLMs are resumes, legal contracts, leases, newspaper articles, and other documents with unstructured text.

The following prompt instructs Claude 3 Haiku to extract information from short text like posts on social media, although it can be used for much longer pieces of text like legal documents or manuals. In the following example, we use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

Retrieval Augmented Generation (RAG) is an approach in natural language generation that combines the strengths of information retrieval and language generation models. In RAG, a retrieval system first finds relevant passages or documents from a large corpus based on the input context or query. Then, a language generation model uses the retrieved information as additional context to generate fluent and coherent text. This approach aims to produce high-quality and informative text by using both the knowledge from the retrieval corpus and the language generation capabilities of deep learning models. To learn more about RAG, see What is RAG? and Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart.

The following prompt instructs Claude 3 Haiku to answer questions about a specific topic and use a context from the retrieved information. We use the color code defined earlier to highlight the prompt sections:

Claude 3 Haikus response:

The prompt incorporates the following best practices:

In this post, we explored best prompting practices and demonstrated how to apply them with the Claude 3 family of models. The Claude 3 family of models are the latest and most capable LLMs available from Anthropic.

We encourage you to try out your own prompts using Amazon Bedrock playgrounds on the Amazon Bedrock console, and try out the official Anthropic Claude 3 Prompt Engineering Workshop to learn more advanced techniques. You can send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

Refer to the following to learn more about the Anthropic Claude 3 family:

David Laredo is a Prototyping Architect at AWS, where he helps customers discover the art of the possible through disruptive technologies and rapid prototyping techniques. He is passionate about AI/ML and generative AI, for which he writes blog posts and participates in public speaking sessions all over LATAM. He currently leads the AI/ML experts community in LATAM.

Claudia Cortes is a Partner Solutions Architect at AWS, focused on serving Latin American Partners. She is passionate about helping partners understand the transformative potential of innovative technologies like AI/ML and generative AI, and loves to help partners achieve practical use cases. She is responsible for programs such as AWS Latam Black Belt, which aims to empower partners in the Region by equipping them with the necessary knowledge and resources.

Simn Crdova is a Senior Solutions Architect at AWS, focused on bridging the gap between AWS services and customer needs. Driven by an insatiable curiosity and passion for generative AI and AI/ML, he tirelessly explores ways to leverage these cutting-edge technologies to enhance solutions offered to customers.

Gabriel Velazquez is a Sr Generative AI Solutions Architect at AWS, he currently focuses on supporting Anthropic on go-to-market strategy. Prior to working in AI, Gabriel built deep expertise in the telecom industry where he supported the launch of Canadas first 4G wireless network. He now combines his expertise in connecting a nation with knowledge of generative AI to help customers innovate and scale.

Originally posted here:
Prompt engineering techniques and best practices: Learn by doing with Anthropic's Claude 3 on Amazon Bedrock ... - AWS Blog

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Amazon Q Businessis a generative AI-powered assistant that can answer questions, provide summaries, generate content, and extract insights directly from the content in digital as well as scanned PDF documents in your enterprise data sources without needing to extract the text first.

Customers across industries such as finance, insurance, healthcare life sciences, and more need to derive insights from various document types, such as receipts, healthcare plans, or tax statements, which are frequently in scanned PDF format. These document types often have a semi-structured or unstructured format, which requires processing to extract text before indexing with Amazon Q Business.

The launch of scanned PDF document support with Amazon Q Business can help you seamlessly process a variety of multi-modal document types through the AWS Management Console and APIs, across all supported Amazon Q Business AWS Regions. You can ingest documents, including scanned PDFs, from your data sources using supported connectors, index them, and then use the documents to answer questions, provide summaries, and generate content securely and accurately from your enterprise systems. This feature eliminates the development effort required to extract text from scanned PDF documents outside of Amazon Q Business, and improves the document processing pipeline for building your generative artificial intelligence (AI) assistant with Amazon Q Business.

In this post, we show how to asynchronously index and run real-time queries with scanned PDF documents using Amazon Q Business.

You can use Amazon Q Business for scanned PDF documents from the console, AWS SDKs, or AWS Command Line Interface (AWS CLI).

Amazon Q Business provides a versatile suite of data connectors that can integrate with a wide range of enterprise data sources, empowering you to develop generative AI solutions with minimal setup and configuration. To learn more, visit Amazon Q Business, now generally available, helps boost workforce productivity with generative AI.

After your Amazon Q Business application is ready to use, you can directly upload the scanned PDFs into an Amazon Q Business index using either the console or the APIs. Amazon Q Business offers multiple data source connectors that can integrate and synchronize data from multiple data repositories into single index. For this post, we demonstrate two scenarios to use documents: one with the direct document upload option, and another using the Amazon Simple Storage Service (Amazon S3) connector. If you need to ingest documents from other data sources, refer to Supported connectors for details on connecting additional data sources.

In this post, we use three scanned PDF documents as examples: an invoice, a health plan summary, and an employment verification form, along with some text documents.

The first step is to index these documents. Complete the following steps to index documents using the direct upload feature of Amazon Q Business. For this example, we upload the scanned PDFs.

You can monitor the uploaded files on the Data sources tab. The Upload status changes from Received to Processing to Indexed or Updated, as which point the file has been successfully indexed into the Amazon Q Business data store. The following screenshot shows the successfully indexed PDFs.

The following steps demonstrate how to integrate and synchronize documents using an Amazon S3 connector with Amazon Q Business. For this example, we index the text documents.

When the sync job is complete, your data source is ready to use. The following screenshot shows all five documents (scanned and digital PDFs, and text files) are successfully indexed.

The following screenshot shows a comprehensive view of the two data sources: the directly uploaded documents and the documents ingested through the Amazon S3 connector.

Now lets run some queries with Amazon Q Business on our data sources.

Your documents might be dense, unstructured, scanned PDF document types. Amazon Q Business can identify and extract the most salient information-dense text from it. In this example, we use the multi-page health plan summary PDF we indexed earlier. The following screenshot shows an example page.

This is an example of a health plan summary document.

In the Amazon Q Business web UI, we ask What is the annual total out-of-pocket maximum, mentioned in the health plan summary?

Amazon Q Business searches the indexed document, retrieves the relevant information, and generates an answer while citing the source for its information. The following screenshot shows the sample output.

Documents might also contain structured data elements in tabular format. Amazon Q Business can automatically identify, extract, and linearize structured data from scanned PDFs to accurately resolve any user queries. In the following example, we use the invoice PDF we indexed earlier. The following screenshot shows an example.

This is an example of an invoice.

In the Amazon Q Business web UI, we ask How much were the headphones charged in the invoice?

Amazon Q Business searches the indexed document and retrieves the answer with reference to the source document. The following screenshot shows that Amazon Q Business is able to extract bill information from the invoice.

Your documents might also contain semi-structured data elements in a form, such as key-value pairs. Amazon Q Business can accurately satisfy queries related to these data elements by extracting specific fields or attributes that are meaningful for the queries. In this example, we use the employment verification PDF. The following screenshot shows an example.

This is an example of an employment verification form.

In the Amazon Q Business web UI, we ask What is the applicants date of employment in the employment verification form? Amazon Q Business searches the indexed employment verification document and retrieves the answer with reference to the source document.

In this section, we show you how to use the AWS CLI to ingest structured and unstructured documents stored in an S3 bucket into an Amazon Q Business index. You can quickly retrieve detailed information about your documents, including their statuses and any errors occurred during indexing. If youre an existing Amazon Q Business user and have indexed documents in various formats, such as scanned PDFs and other supported types, and you now want to reindex the scanned documents, complete the following steps:

"errorMessage": "Document cannot be indexed since it contains no text to index and search on. Document must contain some text."

If youre a new user and havent indexed any documents, you can skip this step.

The following is an example of using the ListDocuments API to filter documents with a specific status and their error messages:

The following screenshot shows the AWS CLI output with a list of failed documents with error messages.

Now you batch-process the documents. Amazon Q Business supports adding one or more documents to an Amazon Q Business index.

The following screenshot shows the AWS CLI output. You should see failed documents as an empty list.

The following screenshot shows that the documents are indexed in the data source.

If you created a new Amazon Q Business application and dont plan to use it further, unsubscribe and remove assigned users from the application and delete it so that your AWS account doesnt accumulate costs. Moreover, if you dont need to use the indexed data sources further, refer to Managing Amazon Q Business data sources for instructions to delete your indexed data sources.

This post demonstrated the support for scanned PDF document types with Amazon Q Business. We highlighted the steps to sync, index, and query supported document typesnow including scanned PDF documentsusing generative AI with Amazon Q Business. We also showed examples of queries on structured, unstructured, or semi-structured multi-modal scanned documents using the Amazon Q Business web UI and AWS CLI.

To learn more about this feature, refer toSupported document formats in Amazon Q Business. Give it a try on theAmazon Q Business consoletoday! For more information, visitAmazon Q Businessand theAmazon Q Business User Guide. You can send feedback toAWS re:Post for Amazon Qor through your usual AWS support contacts.

Sonali Sahu is leading the Generative AI Specialist Solutions Architecture team in AWS. She is an author, thought leader, and passionate technologist. Her core area of focus is AI and ML, and she frequently speaks at AI and ML conferences and meetups around the world. She has both breadth and depth of experience in technology and the technology industry, with industry expertise in healthcare, the financial sector, and insurance.

Chinmayee Rane is a Generative AI Specialist Solutions Architect at AWS. She is passionate about applied mathematics and machine learning. She focuses on designing intelligent document processing and generative AI solutions for AWS customers. Outside of work, she enjoys salsa and bachata dancing.

Himesh Kumar is a seasoned Senior Software Engineer, currently working at Amazon Q Business in AWS. He is passionate about building distributed systems in the generative AI/ML space. His expertise extends to develop scalable and efficient systems, ensuring high availability, performance, and reliability. Beyond the technical skills, he is dedicated to continuous learning and staying at the forefront of technological advancements in AI and machine learning.

Qing Wei is a Senior Software Developer for Amazon Q Business team in AWS, and passionate about building modern applications using AWS technologies. He loves community-driven learning and sharing of technology especially for machine learning hosting and inference related topics. His main focus right now is on building serverless and event-driven architectures for RAG data ingestion.

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Improve productivity when processing scanned PDFs using Amazon Q Business | Amazon Web Services - AWS Blog