Better Together: Humanity + Machine Learning

Andreessen Horowitz

Artificial intelligence has not only become an international arms race, competition has now heated up as companies look to adopt machine learning/deep learning at an unprecedented pace.

Is Machine Learning Overhyped?

Tom Tunguz

2016 was the year of machine learning. During last quarter of 2016, machine learning research has made huge strides. While some may groan that every pitch deck is littered with the words machine learning or artificial intelligence, I think each deck ought to be. Because over the next five to ten years, nearly every company will use machine learning in some form.

AWS beefs up SageMaker machine learning

IT World

Amazon Web Services has expanded the capabilities of its Amazon SageMaker machine learning toolkit to address a number of challenges that enterprises confront when trying to operationalize machine learning, from model organization, training, and optimization to monitoring the performance of models in production.

Machine learning isn’t as hard as it looks

Inside Intercom

It’s easy to believe that machine learning is hard. After all, you’re teaching machines that work in ones and zeros to reach their own conclusions about the world. Indeed, the majority of literature on machine learning is riddled with complex notation, formulae and superfluous language. As Intercom’s own machine learning expert, Fergal Reid , puts it, machine learning is basically a branch of applied statistics.

The AI Agency - A Novel GTM for Machine Learning SaaS Startups

Tom Tunguz

AI Agencies use machine learning to disrupt a market dominated by agencies. Often, these startups begin as software companies selling machine learning software into agencies. The startup leverages machine learning under the hood. Starting an AI Agency has two important benefits for machine learning companies. As they operate, AI agencies create high quality data for training machine learning models.

Reducing Churn With Machine Learning Powered Push Messages

Ryan Berg

Machine learning RetentionAccording to research conducted by UrbanAirship, 95% of opt-in users who don’t receive a push notification in the first 90 days will churn.

When Machine Learning Just Isn't Enough

Tom Tunguz

At SaaStr earlier this year, I spoke about the huge potential of machine learning in SaaS. Only by nailing the workflow will a user grant you the time and permission to wow them with machine learning. How can software improve a current workflow to such an extent that a user is willing to stop their current workflow and learn a new one? Machine learning enables startups to inject a new type of magic to their product.

Machine Learning in Consumer Products

Tom Tunguz

I believe machine learning will drive the next big wave of innovation in consumer web services. But ML and deep learning have reached a point that makes it harder and harder to pull back the curtain and expose weak technology.

Smart Home, Machine Learning, and Discovery

Andreessen Horowitz

… AI, machine & deep learning IoT (internet of things

Managing a User's Trust with Machine Learning SaaS Software

Tom Tunguz

Machine learning SaaS startups face another trust risk – one introduced by probability. Many machine learning systems also rely on probability. A programmer encodes a threshold into machine learning models. Machine learning SaaS companies must find equilibrium on this Goldilocks slackline. Not too strict, not too lenient of a machine learning system.

How Machine Learning Can Benefit Your SaaS Startup

Tom Tunguz

From the millions of Amazon Alexas to the self-driving car, new products are coming to market infused with machine learning. The innovation offered by machine learning techniques are real, and they will changed the SaaS world. How can startups use machine learning to their advantage? There are four broad applications of machine learning: Optimize - this morning, fastest way to travel from Sand Hill Road to South Park in San Francisco is highway 101.

Ethics in Machine Learning - An Opportunity for Startups to Lead

Tom Tunguz

It’s one of undoubtedly many technologies which will use one machine learning model to detect another machine learning model. But I’m hopeful that many machine learning startups who develop novel technologies will also adopt ethics statements.

How to Identify a SaaS Market that Machine Learning Will Disrupt

Tom Tunguz

In SaaS, machine learning has become an essential component to many different products. Whether it’s automating responses to inbound sales queries, identifying expense reports for audit, or surfacing anomalies in data, machine learning improves workflow software. To date, most software imbued with machine learning reduces costs rather than increase revenues. Because machine learning is focused on efficiency gains.

The Key Ingredient to Disrupting with Machine Learning

Tom Tunguz

Which are the ripest areas for startups to disrupt using machine learning? At the core, machine learning/artificial intelligence relies on two key ingredients: advanced algorithms and data sets to train those algorithms. Consequently, proprietary data sources that are essential to train next-generation machine learning models are easier to amass in enterprise rather than consumer.

Machine Learning Predictions for Subscription Companies

ReSci

Machine learning can help marketers of subscription e-commerce businesses by providing predictive insights. The post Machine Learning Predictions for Subscription Companies appeared first on ReSci.

a16z Podcast: The History and Future of Machine Learning with Professor Tom Mitchell

Andreessen Horowitz

How have we gotten to this point with machine learning? In this episode of the a16z podcast, a16z Operating Partner Frank Chen asks these (and many other questions) to one of the OG researchers and … AI, machine & deep learningAnd where are we going?

What the Online Advertising World Can Teach Us about the Evolution of Machine Learning in SaaS

Tom Tunguz

With machine learning, we may see another evolution of this. Machine learning startups create models based on data provided by customers. Unlike the first wave of SaaS software, machine learning startups benefit from the data their customers share with them. Many times, machine learning startups create one global machine learning model that is used across the customer base.

Automating machine learning for platform fraud detection

wepay

At WePay, it increasingly also means machine learning models which can spot complicated fraud patterns faster with less human intervention. This is something we talked a bit about a few months ago, in a blog post about machine learning and shell selling. Today, we’re looking at another challenge we face as we use machine learning to fight fraud in the real world: adapting our models quickly enough to keep up with the attacks we face.

Efficient Merchandising Using Machine Learning Algorithms

Backlinkfy

Machine Learning and AI are set to assume incredibly prominent roles in the retail sector in the not so distant future. Machine Learning in Retail The retail space is undergoing a paradigm shift with innovative applications being tested out for machine learning and AI.

Few Thoughts on Machine Learning Agreements or AI Agreements

Aber Law Firm

Machine learning agreements or AI agreements are super new, and actually very interesting. Not much has been written on these agreements, so I thought I would share a few thoughts on the big issues (from the perspective of the AI/machine learning software vendor).

Automating Machine Learning Monitoring [RS Labs]

ReSci

This blog takes a small dive into one of our internal monitoring tools that overlooks our entire ETL pipeline and helps us stay on top of our machine learning models. The post Automating Machine Learning Monitoring [RS Labs] appeared first on ReSci. Background: Imagine if what viral polite grandma was thinking when she was typing in her. RS Labs

How we’re using machine learning to fight shell selling

wepay

In this first in an occasional series, we’re taking a look at machine learning initiatives at WePay — the kinds of problems we use machine learning for, how we build technology to address them, and how the unique challenges of the payments industry shape our approach. Since shell selling a common problem, and one that’s difficult for humans to spot, we decided to build a machine learning algorithm to help us catch it.

Evaluating Machine Learning Predictions: Customer Churn & CLV [RS Labs]

ReSci

At Retention Science, we are committed on making machine learning and artificial intelligence more accessible and understandable. The post Evaluating Machine Learning Predictions: Customer Churn & CLV [RS Labs] appeared first on ReSci. This blog introduces our process of evaluating the accuracy of two crucial predictive models, Customer Churn Prediction and Customer Future Value (CFV). These two predictions provide invaluable insights.

Banking on the Future: Why our most hated institutions will become our most beloved

Andreessen Horowitz

APIs fintech Uncategorized banking credit debt financial services machine learningIt’s expensive to be poor. . There are two banking systems in the world today: one for people with money (or good credit), and another for people without. But neither of these systems work well. People with money have gotten used … The post Banking on the Future: Why our most hated institutions will become our most beloved appeared first on Andreessen Horowitz.

Pinterest’s New Tech: Machine Learning, Product Recs for Smarter Social Commerce

ReSci

Popular social network Pinterest announced on Wednesday it acquired Kosei, a startup company specializing in machine learning and product recommendations. The post Pinterest’s New Tech: Machine Learning, Product Recs for Smarter Social Commerce appeared first on ReSci Pinterest launched….

Microsoft’s Lili Cheng on making bots more human

Inside Intercom

We’re interested in talking to companies, figuring out their core challenges, and then learning what kind of apps and things they want for their customers inside their companies. People are great at learning new things, ambiguous things, and complex problems.

We are still fretting about AI and Regulation in Fintech: WePay’s Week in Payments

wepay

The other constant topic derives from a couple of underlying drivers for FinTech – AI and machine learning – both of which are enablers in the same way they are for many other disruptive sectors. That in turn means FinTech is a great consumer of AI and machine learning.

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Five Reasons to Sell End-to-End Products in Early Markets

Tom Tunguz

Second , in early markets, most of the buyers don’t understand the nuances of the technology, whether it’s IoT platforms, or machine learning infrastructures, or data lakes. Imagine you have just written machine learning model that prices stocks better than anything else in the market. Which will generate more value: selling that machine learning model to existing public market investors, or starting a hedge fund?

The Week In Cloud: June 2

SaaStr

The power of Amazon Textract is that it accurately extracts text and structured data from virtually any document with no machine learning experience required”. Join our Quora group to get all of The Week in Cloud updates throughout the week. I never knew.

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The Free Trial Survey on the Saastr Podcast and Hitting a Blogging Milestone

Tom Tunguz

Last week, the dynamic Harry Stebbings and I recorded episode 213 of the Saastr podcast , where we discussed the learnings from the free trial survey in a bit more detail. The part I can’t overcome is I studied machine learning and our teams used it at Google to great success.

Feature Engineering: A Closer Look, Part 1 [RS Labs]

ReSci

In simple terms, feature engineering involves feeding knowledge into a Machine Learning model. As a refresher, a machine learning model is an algorithm that takes features as input and produces as output a prediction or classification.

The Future of Machine Intelligence

Tom Tunguz

The Future of Machine Intelligence is a free collection of 10 interviews machine learning experts filed by David Beyer. The interviews explain exactly where we are with the state-of-the-art, the challenges to advanced machine learning, and some of the applications. In fact, this summarization ability isn’t limited to graphical content: Perhaps five years is pushing it, but the notion of a machine reading a book for comprehension is not too distant.

Network monitoring in the hybrid cloud/multi-cloud era

IT World

Today’s digital business era requires a more holistic view of networks with the ability to glean and correlate data from diverse cloud environments using big data analytics and machine learning. Network monitoring in the enterprise has never been easy.

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With Smart Software, Sell Ironman Not Robocop

Tom Tunguz

When buying machine learning enabled software, it’s easier to sell like Ironman than Robocop; a product that complements and augments the user’s skills rather than a true replacement. As machine learning continues to become a key differentiator among SaaS products, a secular and positive trend, startups are learning how to sell the promise of the software better and better. And machine learning is becoming another.

DeepCode taps AI for code reviews

IT World

Discover InfoWorld’s 2019 Bossie Award winners: The best open source software for software development, cloud computing, data analytics, and machine learning. ]. DeepCode learns from open source code bases and has built up a knowledge base to make suggestions on improving code. By leveraging artificial intelligence to help clean up code, DeepCode aims to become to programming what writing assistant Grammarly is to written communications.

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AI Superpowers - A History of Chinese Startups and the Implications for the Future of Startupland

Tom Tunguz

The first is his view of the influence of machine learning in the world. First, the book embraces the idea that machine learning creates monopolies based on data aggregation. I remember the first time I visited China.

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How AI is Changing the Sales Process

InsightSquared

Using more advanced machine learning programs and through the input of historical and transactional customer data, these programs help build propensity models for sales forecasting, customer acquisition, and retention strategies.

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