In The News

GenAI is not yet as advanced as AI focused on diagnosis and predictions.

FAIR powered by Aible leverages the power of generative AI to provide deep insights through a proprietary conversational interface

Customers are now instantly be able to leverage the enterprise-grade generative AI solution to further enhance data engineering and analytics efforts while automatically addressing key shortcomings of generative AI for enterprise use including data privacy & residency, hallucinations, customization to enterprise data, etc. Business teams can now gain trial access to generative AI for enterprise analytics with ChatAible here.

Innovation in AI has prompted talk of a coming technological leap as profound as the creation of the internet. It has also sparked anxiety. CFOs need to navigate past the hype and fear.

Why are some firms better and faster than others at adapting their use of customer data to respond to changing or uncertain marketing conditions?

Aible, the only enterprise AI solution that guarantees impact in 30 days, today announced new capabilities that augment leading Generative AI technology such as GPT-4 and Google's Vertex AI with Aible's ability to completely automate enterprise analytics.

What a week—perhaps the most eventful week in A.I. (at least, in terms of sheer volume of announcements) that I can remember in the past seven years writing about this topic.

Aible today announced new capabilities that augment leading Generative AI technology such as GPT-4 and Google’s Vertex AI with Aible’s ability to completely automate enterprise analytics.

In just a few months, you’ll be able to ask a virtual assistant to transcribe meeting notes during a work call, summarize long email threads to quickly draft suggested replies, quickly create a specific chart in Excel, and turn a Word document into a PowerPoint presentation in seconds.

The future of artificial intelligence (AI) will be open. New and innovative capabilities in areas like generative AI will come from all corners of the technology ecosystem and from companies distributed around the globe — from early stage startups to cloud-native AI platforms to large, global enterprises. The momentum in this space is incredible.

With this launch, enterprises will be able to securely deploy the end-to-end serverless solution for augmented data engineering, analytics, data science and machine learning in their Google Cloud accounts within 15 minutes.

Data is the new oil, but raw data is no good in and of itself. Like oil, data assets have to be gathered entirely and accurately and sent through different refining processes to create value for end users. This is the general data lifecycle — an area where artificial intelligence (AI) is going to play a major role for enterprises.

Can artificial intelligence make us more creative and innovative? It’s a subject of hot debate and discussion. A recent analysis out of the Gottlieb Duttweiler Institute suggests that, yes, AI can help us expand our range of innovation.

ChatGPT promises to democratize artificial intelligence, and is already making it relatively easy for non-data-scientist types to partake in the wonders of machine-generated wisdom. Google and Microsoft are following suit and souping up their search engines. Does this portend reduced demand for AI talent?

Aible is the leader in generating business impact from AI in less than 30 days by helping teams go from raw data to business value with solutions for customer acquisition, churn prevention, demand prediction, preventative maintenance, and more.

Data leaders often mention data as a critical challenge in demonstrating value, but that's only sometimes true, according to Vernocchi. Value-first and data-driven are EY's mantras, he says, and he explains them as follows:

The biggest challenge with enterprise AI insights is viewing the technology as a cultural shift — not an isolated tool.

Artificial intelligence (AI) has gradually become accepted by colleges and universities as an effective tool for automating a number of tasks effectively and efficiently.

This Intel executive thinks AI applications will be essential to extracting future insights from the onslaught of data modern education creates.

In a recent Eye on A.I. newsletter, I discussed the abysmal success rate of many A.I. efforts within companies.

With Aible to Prove Out a Scalable Data Science Center of Excellence

Arijit Sengupta once wrote an entire book titled Why A.I. is A Waste of Money. That’s a counter intuitive title for a guy who makes his money selling A.I. software to big companies.

Companies are rushing to invest in AI — but less than 20% of AI investments are resulting in the transformations that AI promises. VB Transform 2022 brought together business leaders from Intel, Wayfair, Red Hat and Aible to discuss how they’re beating the odds to actually harness the full value of AI.

“Supply chain,” once an insider industry term, is now a common phrase uttered by consumers across the globe — and for good reason.

Aible Benchmark Study, a collaboration that is helping enterprises fast-track benefits from advanced analytics and AI, while also evaluating server vs. serverless architecture.

The summit brings together leading technology companies to address the most significant challenges that data analytics and data science leaders face as they build the organizations of the future.

Aible, a cloud-based enterprise AI solution provider, today announced a new collaboration with Intel to help enterprises fast-track benefits from AI.

Students With No Prior Data Science Experience Use Aible to Beat Trained Data Scientists at the UC Berkeley Real World AI Challenge

VCs have a detailed playbook for investing in software-as-a-service (SaaS) companies that has served them well in recent years.

— Don't Forget The Wisdom Of The Crowd

China’s internet watchdog, the Cyberspace Administration of China (CAC), recently issued a draft proposal of regulations to manage how technology companies use algorithms when providing services to consumers.

AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of artificial intelligence technology.

Most AI practitioners are training their models in sterile environments using cleaned data. But future, real-world data will always be imperfect.

Artificial intelligence and machine learning can significantly improve speed and productivity across a variety of industries.

Aible Positioned Furthest In The Visionary Quadrant In The 2021 Gartner Magic Quadrant for Cloud AI Developer Services Report

AI firm Aible is teaming up with Boomi to marry the power of AI with some 1500 pre-built integrations. The goal is to let enterprise professionals across teams easily collaborate on AI-powered end-to-end workflows.

The vaccine rollout at Stanford Medicine was botched with an unfair algorithm. But the technology itself, if implemented fairly and thoughtfully, could be a boon to an organizational mess.

Analytics can exhibit biases that affect the bottom line or incite social outrage through discrimination. It's important to address those biases before problems arise.

Unique Solution Places Low-Code AI with Business Users, Simplifying Tasks and Accelerating Time to Value

Leverage machine learning models in Tableau with the Aible dashboard extension

The Democratic VP nominee has shown an interest in how AI and facial recognition can open the door to bias. Here’s what Joe Biden and Kamala Harris’s AI regulation strategy should consider.

Technology executives are finding ways for their teams to collaborate and be productive as fully remote work persists during the coronavirus pandemic.

Presumptive Democratic presidential nominee Joe Biden’s selection of Sen. Kamala Harris as his running mate suggests Big Tech might avoid the hammer blow of a breakup if the ticket wins the White House.

Sammy Awards honor innovation in connecting and collaborating with prospects and customers,Aible’s real-world AI approach delivers measurable business impact,Company pulls in third major industry accolade in 15 months

The Aible founder’s unsettling experience with Google’s advertising AI reveals the importance of ensuring there is human oversight of automated systems.

The most recent battle between Twitter and President Donald Trump predictably resulted in a partisan divide. Many of the president's supporters argued that his tweets should be posted without editorial comment, while most of the president's detractors strongly supported Twitter's moves to flag tweets it judges to be false or harmful.

The chief data officer role is appearing in more industries and changing in responsibilities. Experts talk about the ways the position is evolving across enterprises.

Today we are torn between two apparently contradictory positions on COVID-19. One camp says, "We need to focus on saving lives first. Choosing between lives and the economy is a false choice because if millions die, the economy will be ruined anyway." The other side says, "The cure is worse than the disease. Let's focus on which activities are safe as opposed to which activities are essential and open up the economy ASAP."

There’s lots of buzz about artificial intelligence, but as Arijit Sengupta, founder and CEO of Aible, points out, “Everyone has heard a lot about AI, but the AI we’ve been hearing about is not the AI that delivers business impact.” Where is AI headed? Why is a single AI model no longer the right approach? How can your enterprise make the most of this technology?

Machine learning has been used successfully in many disciplines that increasingly depend on it. However, the success relies on human machine learning experts to perform many tasks, according to an account on AutoML.org, a website of the community.

When it comes to making businesses run better, artificial intelligence has shown more promise than performance.

There are all kinds of clichés thrown around technology conferences at the moment. Here are two about data that are currently doing the rounds: "Data is the new oil" and "garbage data in, garbage insights out", a reference to the impact of feeding "dirty" data into the machine learning and artificial intelligence tools.

The AI dystopia that many of us worry about is a world of robot overlords and nuclear wars waged with autonomous weapons, what Elon Musk, the billionaire chief executive of the electric carmaker Tesla, has called “an immortal dictator from which we can never escape.” But as someone who has spent years studying and developing AI, I fear that the real dangers of AI may be much more subtle, but no less dehumanizing.

As we approach 2020 & reflect on the state of Data Science, Analytics, and Machine Learning, I'd like to make 3 Predictions for the coming year. Many businesses will come to realize how to begin truly implementing AI as early adopters realize value and the hype metamorphosizes into reality. I'll also explain how my new company, Aible, fits into these predictions.

TechRepublic's Karen Roby talks with an AI expert who believes we need to rethink our approach and focus more on cost benefit tradeoffs and resourcing.

AI models are trained for accuracy not business results. Aible is recruiting data scientists and developers to change that situation.

Aible Advanced empowers data scientists with a platform that advances beyond legacy semi-AutoML solutions such as DataRobot to enable data scientists to achieve business impact quickly and efficiently. Aible Advanced automates all the repetitive and boring parts of the end-to-end data science and model deployment process so data scientists and developers can focus on the parts where they add the most value.

A series of Q&A interviews with innovators operating at the intersection of consumer behavior and business transformation: Arijit Sengupta, founder and CEO, Aible, which makes AI accessible to anyone with a business problem to solve and curiosity, with no previous training or data science experience required.

Artificial intelligence can be highly accurate, yet still have your business leaving money on the table, according to Arijit Sengupta, founder and CEO of Aible, a just-launched San Francisco-based startup.

If you’re building your machine learning models to optimize accuracy, then you’re probably doing it wrong. That’s the message coming from Aible, an ML company that emerged from stealth today with new software designed to optimize ML decision-making around economic value, not accuracy.

Imagine you are selling cookies. Data Science asks a prediction question: "Who is most likely to buy?" However, the business user actually asks a business optimization question: "How do I make the most money?" The answers to these two questions are not identical and that is the entire problem with evaluating AI without considering business impact.

Aible, the innovators of AI for business impact, today announced a partnership with officially launched earlier today, announcing the industry’s only Real World AI trained for measurable business impact, not simply accuracy. To date, AI has been trained to achieve the highest accuracy, but this unrealistically assumes that all costs and benefits are equal. In business, the benefit of a correct prediction is never equal to the cost of a wrong one. An AI trained for accuracy would never recognize this cost-benefit tradeoff.

Today Aible launched the industry’s only Real World AI trained for business impact, not accuracy. Aible lets business users create custom AI based on real cost-benefit tradeoffs and operational constraints. This innovation shows business users the expected business impact of the AI before deploying it. Aible makes AI accessible to anyone with knowledge of their business, no data science experience required.

Aible, the innovators of AI for business impact, today announced the UC Berkeley Real World AI Challenge winners — the top scorers include two high school students, a history major and no data scientists.