Women are trailblazing new ideas across industries, countries and cultures every day. This International Women’s Day, take a minute to read some innovative SAS Hackathon highlights for this upcoming year and from the past. You'll also learn about some incredible women and see what amazing innovations lie ahead. AI
Editor's note: This article follows Curious about ChatGPT: Exploring the origins of generative AI and natural language processing. As ChatGPT has entered the scene, many fears and uncertainties have been expressed by those working in education at all levels. Educators worry about cheating and rightly so. ChatGPT can do everything
Generative AI (GenAI) is a category of AI that can create new content, including video, audio, images and text. GenAI has the potential to change the way we approach content creation. It’s gotten much attention lately. Take ChatGPT for example. The AI chatbot has captivated the public’s imagination with clever
SAS' Mary Carter details challenges and benefits of accelerating the delivery of SAS software.
Students in the master's program in data science at the University of North Carolina Wilmington (UNCW) drove real-world results using SAS® Viya® for the labor market analytics company Lightcast. The project gave students practical analytical tools to solve a business challenge – invaluable career preparation. At the same time, Lightcast gained business insights
Responsible innovation is critical because technology does not exist in a vacuum. It affects us all in unexpected ways. We know analytics has an undeniable impact on society. For example, analytics can help hospitals manage their inventories for essential items like wheelchairs and bladder scanners, help sports teams curate a
IDC 마켓스케이프 보고서에서 MLOps 플랫폼 부문 리더 제품으로 선정된 ‘SAS Model Manager’, DataRobot, Databricks, Dataiku, Domino Data Lab등에 성능 우위 입증 다양한 비즈니스 영역에서 머신러닝의 적용이 점차 활기를 띄며 증가하고 있는 가운데, 많은 IT 리더들은 인공지능과 머신러닝 기술을 선택하고 구현하기 위한 필수 기술로 ‘모델 옵스(이하 ModelOps)’를 지목하고 있습니다. AI타임즈에 따르면,
Welcome to the continuation of my series Getting Started with Python Integration to SAS Viya. In this post I'll discuss how to bring a distributed CAS table back to your Python client as a DataFrame. In this example, I'm using Python on my laptop (Python client) to connect to the
How did we get to a place where a conversational chatbot can quickly create a personalized letter? Join us as we explore some of the key innovations over the past 50 years that help inform us about how to respond and what the future might hold.
Time series data is widely used in various fields, such as finance, economics, and engineering. One of the key challenges when working with time series data is detecting level shifts. A level shift occurs when the time series’ mean and/or variance changes abruptly. These shifts can significantly impact the analysis and forecasting of the time series and must be detected and handled properly.
Did you know that about 8% of the world's men are colorblind? (More correctly, 8% of men are "color vision deficient," since they see colors, but not all colors.) Because of the "birthday paradox," in a room that contains eight men, the probability is 50% that at least one is
The past few years have been ripe with uncertainty, further proving the necessity of resilience across industries. While the hospitality sector has faced significant disruption over the last two years, it also faces several new opportunities. Consumers are turning their focus to self-improvement. Wellness trends continue to grow in popularity.
Since 2008, SAS has supported an interface for calling R from the SAS/IML matrix language. Many years ago, I wrote blog posts that describe how to call R from PROC IML. For SAS 9.4, the process of installing R and calling R from PROC IML is documented in the SAS/IML
SAS' Marinela Profi and Sophia Rowland elaborate on IDC including SAS among the leading platform providers for Machine Learning Operations.
SAS Explore was created by technologists for technologists, and has more than 100 free SAS training sessions. Here are a few top picks!
Anuja Nagpal and Yonglin Zhu of SAS R&D reveal how, MLPA – without any code and within a given timeframe – finds an effective pipeline for a data set after applying data preprocessing, feature engineering and modeling with hyperparameter tuning.
Las previsiones hablan de un 2023 desafiante. Muchos países vivirán tiempos de cambios y de ajustes económicos, de índices de inflación impensados años atrás, de incertidumbres en el manejo de las tasas de cambio y de restricciones a recursos vitales o materias primas producto de las guerras o de las
Billy Dickerson of SAS R&D chronicles three key challenges and lessons learned in SAS' journey to continuous integration (CI) and continuous delivery (CD).
SAS' Bahar Biller and Xi Jiang use the example of a semiconductor manufacturing plant to illustrate the role of sensitivity analysis in assessing supply chain risk.
Los equipos deportivos generan datos de una amplia gama de fuentes. Estos permiten conocer el estado físico y el desempeño de los jugadores, cómo se están manejando las operaciones e incluso la experiencia que están viviendo los aficionados. La analítica les permite obtener los insights que les darán una ventaja
To find exact duplicates, matching all string pairs is the simplest approach, but it is not a very efficient or sufficient technique. Using the MD5 or SHA-1 hash algorithms can get us a correct outcome with a faster speed, yet near-duplicates would still not be on the radar. Text similarity is useful for finding files that look alike. There are various approaches to this and each of them has its own way to define documents that are considered duplicates. Furthermore, the definition of duplicate documents has implications for the type of processing and the results produced. Below are some of the options. Using SAS Visual Text Analytics, you can customize and accomplish this task during your corpus analysis journey either with Python SWAT package or with PROC SQL in SAS.
Whether working as a business analyst, data scientist or machine learning engineer, one thing remains the same – making an impact with data and AI is what really matters. Pre-processing and exploring data, building and deploying models and turning those scoring values into an actionable insight can be overwhelming. A
こんにちは！SAS Institute Japanの堀内です。今回も自然言語処理について紹介いたします。 前回の投稿では、実際にSASを使って日本語の文章を扱う自然言語処理の例を解説しました。 最終回の本投稿ではその応用編として、自然言語処理の代表的なタスクとSASによる実装方法を紹介します。なお、ここでいうタスクとは「定式化され一般に共有された課題」といった意味になります。自然言語処理には複数のタスクがあり、タスクごとに、共通する部分はあるとはいえ、問題解決のアプローチ方法は基本的に大きく異なります。SASには各タスクごとに専用のアクションセット1が容易されています。 要約タスク その名の通り文章を要約するタスクです。SASではtextSummarizeアクションセットで対応可能です。 ここでは、NHKのニュース解説記事「気になる頭痛・めまい 天気が影響？対処法は？」(https://www.nhk.or.jp/kaisetsu-blog/700/471220.html) の本文を５センテンスで要約してみましょう。 import swat conn = swat.CAS('mycashost.com', 5570, 'username', 'password') conn.builtins.loadActionSet(actionSet='textSummarization') conn.textSummarization.textSummarize(addEllipses=False, corpusSummaries=dict(name='corpusSummaries', compress=False, replace=True), documentSummaries=dict(name='documentSummaries', compress=False, replace=True), id='Id', numberOfSentences=5, table={'name':CFG.in_cas_table_name}, text='text', useTerms=True, language='JAPANESE') conn.table.fetch(table={'name': 'corpusSummaries'}) numberOfSentencesで要約文のセンテンス数を指定しています。結果は以下の通りです。 'まず体調の変化や天気、気温・湿度・気圧などの日記をつけ、本当に天気が影響しているのか、どういうときに不調になるのかパターンを把握すると役立ちます。 気温・湿度以外にも、気圧が、体調の悪化や、ときに病気の引き金になることもあります。 私たちの体は、いつも耳の奥にある内耳にあると言われている気圧センサーで、気圧の変化を調整しています。 ただ、天気の体への影響を研究している愛知医科大学佐藤客員教授にお話ししを伺ったところ、「台風最接近の前、つまり、気圧が大きく低下する前に、頭が痛いなど体調が悪くなる人は多い」ということです。 内耳が敏感な人は、わずかな気圧の変化で過剰に反応し、脳にその情報を伝えるので、脳がストレスを感じ、体のバランスを整える自律神経が乱れ、血管が収縮したり、筋肉が緊張するなどして、その結果、頭痛・めまいなどの体に様々な不調につながっているのです。' 重要なセンテンスが抽出されていることが分かります。 テキスト分類タスク 文章をいくつかのカテゴリに分類するタスクです。その内、文章の印象がポジティブなのかネガティブなのか分類するものをセンチメント分析と呼びます。ここでは日本語の有価証券報告書の文章をポジティブかネガティブか判定してみます。使用するデータセットは以下になります。 https://github.com/chakki-works/chABSA-dataset （なお、こちらのデータセットには文章ごとにポジティブかネガティブかを示す教師ラベルは元々付与されておりませんが、文章内の特定のフレーズごとに付与されているスコアを合算することで教師ラベルを合成しております。その結果、ポジティブ文章は1670文章、ネガティブ文章は1143文章、合計2813文章になりました。教師ラベルの合成方法詳細はこちらのブログをご覧ください。） pandasデータフレームにデータを格納した状態を確認してみましょう。 df = pd.read_csv(CFG.local_input_file_path) display(df)
Having the right data analytics platform can help law enforcement solve crimes faster.
Científico de datos lleva años en la lista de carreras más populares pero, ¿cómo se llega a ser científica de datos? Bueno, mi viaje para convertirme en científica de datos no fue un viaje intencional, pues no empecé con un plan para terminar en una carrera de ciencia de datos.
Often the biggest challenge when implementing a successful forecasting process has nothing to do with the analytics. Forecast adoption – incorporating forecasts into decision-making – is just as high a hurdle to overcome as the models themselves. Forecasting is more than analytical models Developing a forecasting process typically begins with
In this introduction to powerful knowledge graph tools, SAS' Brandon Reese shows you how they can predict disease similarity and compound similarity using an unsupervised approach.
High unemployment rates of the post-pandemic era in many recovering regions and increasing inflation rates are signaling an economic recession. With the pressure of increasing energy prices on consumer cash flow and households in many advanced economies facing a cost of living crisis, collections managers know it’s time to transform
AI has, for many years, been the stuff of fantasy. From the monster in Mary Shelley’s Frankenstein to the dystopian futures depicted in films such as Metropolis, the Matrix and Minority Report, the idea of intelligent machines has been capturing the imagination of writers for centuries. Our ability to store
Data is crucial for the development of artificial intelligence (AI) applications. However, the rapid availability of data is a challenge due to increasingly strict privacy regulations. A possible solution is to use synthetic data. Gartner predicts by 2024 that 60% of the data used to develop AI and analytics applications
