With advanced software, these companies are making cognitive computing accessible.

Business applications of cognitive computing are gaining popularity rapidly. Cognitive computing technology combines machine learning, reasoning, NLP, speech vision, and human-computer interaction in a way that mimics the human brain to improve decision-making. This AI-powered capability has the potential to transform several industries, right from sales forecasting, improving communications, supply chain operations, to better drug discovery, marketing, defense, fraud detection, financial sector, and agriculture.

Tech companies that have released these applications are working on preparing products and services to help clients put data to better work.

• OPTIMIZING THE FINANCING SERVICES INDUSTRY WITH COGNITIVE COMPUTING
• UNLOCKING THE POWER OF COGNITIVE COMPUTING IN HUMAN RESOURCES
• ADVANCES IN HEALTHCARE: COGNITIVE COMPUTING

Innovative Cognitive Computing Companies

Aisera

Aesera’s AI Service Management Platform (AISM), helps customers and employees by optimizing processes for better productivity and slashed costs. The platform connects automated service experience with AI-based conversational engagement and workflow automation.

Accenture

Accenture aims to leverage all of its clients and their processes with the company’s unique approach to scaling AI, analytics, data, and automation. With applied intelligence, Accenture’s teams help organizations to invest in the right solutions and services that will suit their business goals.

AWS Machine Learning

AWS’s machine learning services and supporting cloud infrastructure, enable every developer, data scientist, and expert practitioner to use machine learning capabilities. At present, AWS is helping more than a thousand clients accelerate their machine learning capabilities.

Alteryx

Alteryx, provides a platform that facilitates end-to-end analytics process automation. The company recently announced new products that innovatively deal with analytics and data science automation, analytics in the cloud, AI, and machine learning. These new launches focus on delivering a simple user experience with no-code, low-code approaches to leverage business outcomes.

C3 AI

C3 AI, provides enterprise AI software that accelerates digital transformation with fully integrated products like C3 AI Suite (an end-to-end platform for AI applications), C3 AI Applications (a bundle of industry-specific SaaS AI apps), C3 AI CRM (CRM applications for AI and ML), and C3 AI Machina, a no-code AI solution for everyday data science.

SparkCognition

SparkCognition, provides three cognitive computing software for enterprises, SparkPredict, SparkSecure, and MindFabric. SparkPredict uses sophisticated algorithms to large pools of data with intelligence. SparkSecure Cognitive Insights add a cognitive layer to security solutions to improve threat detection, leverage IT abilities, and reduce the probability of false positives. MindFabric platform acts as a workspace for professionals for deep data-led insights.

Microsoft Cognitive Services

Microsoft’s Cognitive Services boosts Microsoft’s machine learning APIs to help developers easily add intelligent features like emotion detection, voice recognition, and language understanding. With just a few lines of code, developers can build apps that can work across devices like iOS, Android, and Windows.

Expert System

Expert System, provides software that is capable of working with language and technology to make sense out of unstructured content. Clients can extract insights and make human-level decisions with strengthened analytics. This software comprehends multiple languages, just like humans.

IBM Watson

IBM Watson performs deep content analysis and uses evidence-based reasoning to leverage and improve decision making, reducing costs for better outcomes. For this, the software uses

A set of transformational technologies that use natural language, hypothesis generation, and evidence-based learning. Experts believe that Watson holds the power to transform the process of business problem solving as the system uses machine learning, statistical analysis, and NLP to find answers amidst the clues. Watson then compares the answers by ranking them based on confidence and accuracy.

Deepmind

Deepmind aims to solve intelligence-based business problems with the research. Deepmind uses real-world applications of AI technology to help industries like healthcare. It enables nurses, doctors, and support staff to quickly analyze test results, forms the right diagnosis and treatment, and escalate the case to a specialist. All these judgments can be made using the advanced technology of accurate analysis.

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Having machine learning in a company’s portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. 

According to PwC, AI’s potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company’s total funding to $47 million. 

For OctoML’s CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. “It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware. Even if your determined machine learning team has hurtled through this gauntlet, they still have to go through a whole different set of challenges to package and deploy at the edge,” explained Ceze.

A good performance in ML models requires long hours of manual optimizations. These long hours will then translate into hefty cloud bills. Added to this is the model packaging which varies with devices and platforms. According to Ceze, there are no modern CI/CD integrations to keep up with model changes.

“What good is an ML model if it isn’t fast? doesn’t scale? isn’t accurate enough? takes weeks to deploy? and costs too much?,” questioned Ceze as he made a case for OctoML. 

OctoML addressed these pain points with their open-source machine learning compiler framework Apache TV, which according to the team, has quickly become the go-to solution for developers and ML engineers to maximize ML model performance on any hardware backend. “With OctoML we are establishing the first Machine Learning Acceleration Platform that will automatically maximize model performance while enabling seamless deployment on any hardware, cloud provider, or edge devices,” said Ceze.

Be it MLOps or XOps, these services are designed to ease the developers of technical debt that these mega ML models accumulate with changing complexities. Apart from OctoML, there are a few other startups that have succeeded in convincing the investors. Let’s take a look at couple of them:

Verta 

Funding till date: $10 million

The team at Verta is building software for data science teams to address the problem of model management — how to track, version, and audit models used across products. Verta MLOps software supports model development, deployment, operations, monitoring, and collaboration enabling data scientists to manage models across their lifecycle. So far, the company has $10 million in funding and it promises to make robust, scalable, mature deployable models a reality.

“We’re obsessed with helping organizations get ML models into production because that’s the only way they can generate business value,” said the team at Algorithmia. Their enterprise MLOps platform manages all stages of the production ML lifecycle within existing operational processes, so you can put models into production quickly, securely, and cost-effectively. Unlike inefficient and expensive do-it-yourself MLOps management solutions that lock users into specific technology stacks, Algorithmia automates ML deployment, optimizes collaboration between operations and development, leverages existing SDLC and CI/CD systems, and provides advanced security and governance.

Today Algorithmia’s services are used by over 130,000 engineers and data scientists, including the United Nations, government intelligence agencies, and Fortune 500 companies.

“It’s [MLOps] going to be an essential component to enterprises industrializing their AI efforts in the future,” said Diego M. Oppenheimer, Algorithmia’s CEO in a recent interview with GitHub.

Databand.ai

Funding: $14.5 million

Databand brings in the similar flavor into the ML ecosystem. The team Databand is trying to solve the problems that arise due to increasing data workloads. The company founded by Josh Benamram, Victor Shafran and Evgeny Shulmanhelps helps data engineering teams catch data pipeline issues and trace the impact of those problems across end-to-end data flows. Databand’s platform includes an application for visualizing pipeline metadata, and an open source library for integrating with your Python, Java, Scala, or SQL data processes. Data pipeline monitoring is a key aspect of machine learning deployment. We can clearly see how targeting even a niche aspect of the whole ML deployment can land big investors.

Modern day software companies are in the process of or have already embraced machine learning as a key tool. Now they are at a crucial juncture where they can either leverage the MLOps services offered by these startups or build everything on their own. But, there are not many reasons why an organization looking to transition to ML will take the pain of MLOps. As companies look to leverage ML minus the deployment headache, niche players like OctoML will  continue to pop up. Even the latest Gartner survey lists scalability and acceleration of machine learning deployment as two driving forces that will continue to trend this year. According to Gartner, XOps— a variant of MLOps that deals with efficiencies in data, machine learning, model, platform will try to implement best DevOps practices and ensure reliability, reusability and repeatability. 

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By 2025, more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics, according to Gartner, Inc.

“Successful investors are purported to have a good “gut feel” — the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner. “However, this “impossible to quantify inner voice” grown from personal experience is decreasingly playing a role in investment decision making. The traditional pitch experience will significantly shift by 2025 as VC and private equity (PE) investors turn to leveraging AI and data science insights for due diligence.”

Gartner predicts that by 2025, the AI- and data-science-equipped VC or PE investor will become commonplace. Increased advanced analytics capabilities are rapidly shifting the early-stage venture investing strategy away from gut feel and qualitative decision making to a more modern platform-based quantitative process. Information gathered from sources such as LinkedIn, PitchBook, Crunchbase and Owler, along with third-party data marketplaces, can be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” said Stakenas.

AI Will Help Determine If Leadership Teams Will Succeed or Fail

Current AI technology is already capable of providing insights into customer desires and predicting future behavior. Unique profiles can be built with little to no human input, which can be further developed via natural language processing AI that can determine qualities about an individual from real-time or audio recordings. While this technology is currently used primarily for marketing and sales purposes, by 2025, investment organizations will be leveraging it to determine which leadership teams are most likely to succeed.

“The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured,” said Mr. Stakenas. “AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success.”

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Apart from its Bull’s Eye Landing, What’s so Unique about Perseverance Rover Mission from NASA?

Last Thursday, NASA’s Perseverance rover grabbed headlines all around the world, with its historic landing on the Martian surface. The rover, which was launched July 30, 2020, from the Cape Canaveral Air Force Station in Florida atop a ULA Atlas 541 rocket, finally made touched down at 3.44pm ET (8.44pm GMT). The mission objective is to search for signs of ancient life and to collect rock and soil samples for possible return to Earth – where it will be scanned for presence of microbial life.

Why Jezero?

The name Perseverance was given by Alex Mather, who won a K-12 public naming contest with 28,000 entries. Named after the human characteristic, the mission follows similar name-scheme of its predecessors: Curiosity, Spirit and Opportunity. The rover made a touch down at an ancient river delta site in Jezero Crater.

Jezero, which means lake in Balkan languages, is believed to be a water filled lake that existed nearly 4 billion years ago. It is reported that this Martian lake was as big and wet as Nevada and California’s Lake Tahoe. The deposits in the crater are rich in clay minerals, which form in the presence of water, meaning life may have once existed there – and such sediments on Earth have been known to store microscopic fossils. The rover will start at base of delta cliffs, before moving across the delta towards what was possibly a shoreline, then climbing up the 610-metre crater rim. It will take about two Earth years (one Mars’ year) to complete half this journey.

But the journey is not smooth ‘sailing’! The Jezero Crater is full of obstacles and dangers to the rover, including boulders, cliffs, sand dunes and depressions, any one of which could end the mission, both in landing and as the rover drives along the surface.

Perseverance vs Curiosity

One of the significant upgrades in Perseverance rover from Curosity is that it has received a gentle tread pattern and larger diameter. The reason behind this upgrade is to prevent the rover from getting stuck in the fine Martian sand, as Curiosity did in 2014, and also protect it from sharp Martian rocks called ventifacts.

Both Curiosity and Perseverance share the same basic body (WEB or Warm Electronics Box) and the same type of power source (radioisotope thermoelectric generators), and both landed using a spectacular overhead crane strategy. According to Matt Wallace, the deputy mission manager at NASA’s Jet Propulsion Lab, Perseverance can drive three times faster than any previous Mars rover. It will have a maximum speed of only 4.4 cm per second, which is one-thirtieth as fast as human walking speed.

The rover is powered by 4.8 kilograms of Plutonium 238, which can supply 110 watts of electrical power continuously. A pair of lithium-ion batteries will work in tandem with the RTG to help handle peak demand.

The last NASA spacecraft to land on Mars was the InSight robotic probe, which touched down on 26 November 2018. While there was no live video, the landing was monitored through telemetry clocking the probe’s velocity. Till now, Nasa has nailed eight of nine landing attempts, making the US the only country to achieve a successful touchdown. Following behind is China, whose Tianwen-1 mission along with UAE’s Hope orbiter made it to Martian orbit few days ago. While Tianwen-1’s rover will attempt to land on Mars in May, Hope will remain in orbit to study the Martian atmosphere.

Even the Soviet Union had successfully made the first impact on Mars on 27 November 1971. Unfortunately, the spacecraft was destroyed. Though Soviet Union did manage a soft landing on Mars a few months later, its spacecraft started to transmit an image back to Earth, before going silent a little over a minute and a half into the transmission.

Interesting Technologies Aboard Perseverance

It is natural to assume that the rover will carry out a number of experiments during its mission. These experiments will leverage a blend of technologies like IoT sensors, robotics, cloud, artificial intelligence and more.

For instance, Perseverance features an automated hazard avoidance system called Terrain Relative Navigation (TRN.) The TRN, or Landing Vision System (LVS), compares real-time images from its camera with an onboard map of the surface in Jezero Crater, Perseverance’s landing site. The map is created from high-definition orbital images of the crater area. If the rover is heading for a hazardous obstacle, it can fire its retrorockets and avoid the hazard.

Perseverance is equipped with a multi-functional instrument called SuperCam. SuperCam contains three separate spectrometers. One of them is called LIBS, or Laser-Induced Breakdown Spectroscopy. The rover also has a drill that uses rotary motion with or without percussion to penetrate the Martian surface to collect the precious samples. The drill is equipped with three different kinds of attachments that allow sample collection and surface analysis. SuperCam also has a microphone. The microphones onboard Perseverance will help scientists record the sounds of its tense “seven minutes of terror” touchdown sequence.

NASA had installed microphone in its Mars Polar Lander spacecraft, and had one built into the Phoenix lander’s descent camera. Sadly, neither mic returned any data. While Mars Polar Lander crashed during its touchdown attempt in December 1999, Phoenix’s descent camera was never turned on due to concerns that its use could complicate the entry, descent and landing (EDL) process. Phoenix which landed in May 2008, had found buried water ice during its successful surface mission.

The rover also has a ground penetrating radar that could detect water up to 10 meters deep. It further, contains multiple science experiments including an electrically powered oxygen generator called MOXIE (Mars Oxygen In-Situ Resource Utilization Experiment), that converts the CO2 from the atmosphere into oxygen. It also has several scientific cameras and a special sensor to protect the robot from incorrectly contacting the surface to reduce the chance of damage.

During the course of its two-year mission, Perseverance will collect up to 43 samples of Martian rock and soil. These samples will be stowed in white tubes on the Martian surface to be returned to Earth on a future planned mission. Official sources report that the sample tubes will be placed in caches on the surface, and the locations of these samples will be catalogued. Orbiter images will identify the sample locations to within one meter (3 ft) and the rover itself will increase that accuracy to within one centimeter. Perseverance also has what are called “witness tubes.” It has five of these tubes, which shall be used to collect molecular and particular contaminants during drilling sessions.

Collecting samples are key part of space-geology and other space sciences. Recently, Japan’s Hayabusa2 spacecraft collected samples from asteroid Ryugu and returned them to Earth. And NASA’s OSIRIS-REx just successfully collected samples from asteroid Bennu. Those samples will be returned to Earth in September 2023.

It robotics arm will also use an artificial intelligence powered device called the Planetary Instrument for X-ray Lithochemistry, or PIXL. PIXL is a lunchbox-size instrument carried at the end of Perseverance’s 7-foot-long robotic arm. Using a coring drill on the end of the arm, the rover will collect core samples from the planet that will be left on the Mars surface for collection by a future mission. A tiny, powerful X-ray beam blast from PIXL can detect over 20 chemical elements by pointing a beam at rocks. The beam produces a telling glow associated with each element present in about 10 seconds.

It also has a partner called Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals or SHERLOC. SHERLOC will seek out organic molecules and minerals, which helps inform science teams of where to collect and cache samples. Its ultraviolet laser will provide a different glow depending on the organic molecules and minerals it detects. And obviously, elementarily, SHERLOC will have his WATSON even on the Martian surface. WATSON, or Wide-Angle Topographic Sensor for Operations and eNgineering, is a camera that can take microscopic images of grains in rock and textures.

Before astronauts step their foot or even enter the atmosphere of Mars, it is crucial to understand the Martian weather and environment conditions they will face. Perseverance’s monitoring system, called Mars Environmental Dynamics Analyzer, called MEDA, is a suite of sensors that will help scientists study weather science, dust and radiation, and how they change over Martian seasons.

Meanwhile, artificial intelligence is used aboard Perseverance for navigation on the planet’s surface. Heather Justice, robotic operations downlink lead at JPL cites that the Perseverance rover will be using the autonomous technology that is used for self-driving cars on Earth. This autonomous technology, officially known as a vision compute element (VCE), will help it do something called “thinking while driving”. It is installed to help Perseverance land itself on Mars and avoid hazards on the Martian surface.

Perseverance also leverages technologies from HPE and Amazon Web Service (AWS). HPE’s specialized, second-generation Spaceborne Computer-2 (SBC-2), will mark the first time that broad AI and edge computing capabilities will be available to researchers on the space station. SBC-2 computer follows the original Spaceborne Computer-1 that was sent to the International Space Station (ISS) in 2017 as part of a validation study to test it in the rigors of space aboard the orbiting laboratory. SBC-1 returned to earth in 2019 after completing its mission. Both Spaceborne Computer-1 and Spaceborne Computer-2 are sponsored by the ISS National Lab.

Also given the massive volume of data that shall be dealt by the Perseverance rover, the Jet Propulsion Lab will store, process, and distribute this high volume of data using cloud features of AWS.

Helicopter Ingenuity

The Perseverance rover will not be alone at Mars. Tagging along is a tiny drone-based helicopter called Ingenuity. Running on LINUX, Ingenuity won’t actually do any science, but it will provide important feedback on flight operations in the thin Martian atmosphere. If successful it will be designated as the first powered flight on any planet other than Earth and to hopefully be the blueprint for future Mars missions.

Designed to last about 30 Martian sols, Ingenuity weighs about 2kg, is 1.2 meters wide, and carries two computers. The drone is more of an experiment than a piece of equipment for scientific discovery; engineers want to test its ability to fly autonomously. It can travel to distances stretching to almost 300 meters, and hover 3 to 4.5 meters from the ground for 90 seconds.

What’s Next?

Mars missions are difficult with a success rate of only 40% so far. However, that has not deterred it from planning its future missions to the Red Planet. For instance, NASA has proposed to send a similar but more capable quadcopter drone to Titan, one of the Moons of the ringed planet Saturn, in 2027. This device is called the Dragonfly mission and it will be nuclear-powered with the ability to fly many kilometers before landing to recharge its battery.

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NEW YORK, Jan. 26, 2021 /PRNewswire/ — Fulcrum Analytics announced their Agile Analytics Lab has been recognized as the Best Big Data Analytics Platform by the 2020 Tech Ascension Awards. 

Fulcrum’s award-winning Agile Analytics Lab is a cloud-based, big data analytics platform supplemented with professional services that allows clients to experiment with software, data sources, and technology not available within their own organization. The Agile Analytics Lab is customized to each client, providing access to the tools and data sources specifically in demand for the organization’s developing data science needs.

The Tech Ascension Awards has recognized the very best innovations in b2c and b2b technology. In the second year of the Tech Ascension Awards, only the most cutting-edge companies stood above the rest. The Tech Ascension awards judged applicants based on technology innovation and uniqueness, market research (analyst reports, media coverage, customer case studies), hard performance stats, and competitive differentiators. The class-leading vendors that received recognition from the Tech Ascension Awards proved their technology solves critical industry challenges and produces invaluable business outcomes for their customers. 

“We are thrilled that our Agile Analytics Lab has been named Best Big Data Analytics Platform by The Tech Ascension Awards,” said Richard Vermillion, CEO of Fulcrum Analytics. “We are proud of the Lab’s role in making our clients’ data science teams more agile. Our Lab provides the technical environment they need to process massive datasets and experiment with cutting edge software and methodologies, without the barrier of having to make large scale investments.”

About Fulcrum Analytics
Fulcrum Analytics has stood at the forefront of data and analytics for over twenty-five years. Fulcrum offers sophisticated data science solutions, groundbreaking applications, and winning strategies that help companies achieve their targeted results. Fulcrum takes on the toughest data challenges to create meaningful business impact and move businesses forward every day. For more information about Fulcrum Analytics, please visit www.fulcrumanalytics.com

About the Tech Ascension Awards
The Tech Ascension Awards elevate companies that possess cutting-edge, innovative technology that solve critical challenges in their respective markets. Applicants are judged based on technology innovation and uniqueness, market research (analyst reports, media coverage, customer case studies), hard performance stats, and competitive differentiators. The awards recognize leaders in cybersecurity, DevOps, big data and consumer technology. For information about the Tech Ascension Awards, please visit www.techascensionawards.com.

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The year 2020: what an absolute doozy. Hopefully, your job is steady despite the circumstances — and cutting it for you in terms of wage and fulfillment. But if that’s not the case, you may need to beef up your CV a little to make it shine for (better paying) potential employers.

If you’ve got your heart set on a lucrative position in tech as a web developer, here’s something to get you started: the 2020 Ultimate Web Developer and Design Bootcamp Bundle. This in-depth training packs 11 courses on a wide array of tools and languages, so you can learn them all or poke around to focus on just the specialties you’re pursuing. At just £30.98, it’s worth it for even just a few of the courses.

The Complete 2020 HTML5 CSS3 Course with Flexbox, Grid, and SASS — This course is called the “Golden Introduction to Web Development” because it teaches you about the structure of a webpage, building with HTML5, and stylising with CS33 so you can confidently design, code, and launch websites at the professional level.

Modern Web Design Complete HTML and CSS — Take your skills up a notch with this course, which covers everything you need to know about front-end web design in HTML5, CSS3, and JavaScript.

The Ultimate HTML Developer — If you want to be a web developer, you’re going to have to get acquainted with HTML. This deep dive will teach you how to write HTML from scratch so you can forget about that CS degree.

Build Responsive Real-World Websites with CSS3 v2.0 — Consider this class your personal CSS masterclass, complete with an e-book and interactive tools.

Create an 8-Bit Style Website — This fun course allows you to create a retro-style 8-bit website to get familiar with portfolio and hobby sites.

Understanding APIs and RESTful APIs Crash Course — By the end of this class, you’ll know the ins and outs of APIs and how computers communicate, so you don’t have to pretend to know what’s going on in team meetings.

JavaScript for Beginners: Learn with 6 Main Projects — Often, Java courses only teach you the programming side of Java. But the truth is, JavaScript is an interactive programming language and you have to get to grips with the interactive side at some point.

Git Essentials: The Step-by-Step Guide to Git and GitHub Mastery — Git is the secret tool among developers since it is so widespread. There are a host of commands you’ll use every day in your new workflow, which are all covered in this course.

JavaScript Essentials — JavaScript fundamentals time. Get fluent in variables, query selectors, functions, and much much more in this course. This training is specially designed to give you transferable programming skills so you can become a full stack developer.

Python for Everybody: The Ultimate Python 2 Bootcamp — Python isn’t called the language that can “do it all” for nothing. Set yourself up for success in every Python-related industry with this one.

Web Design JavaScript Front-End Code Course — This course covers how to select elements from the web page with JavaScript. With mini-projects, you can practice making your website shine.

With 11 courses and over 69 hours of content, you can’t go wrong with the 2020 Ultimate Web Developer and Design Bootcamp Bundle, which is on sale for £30.98 for a limited time.

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As the world struggles to achieve the UN’s Sustainable Development Goals (SDGs), the need for reliable data to track our progress is more important than ever. Government, civil society, and private sector organizations all play a role in producing, sharing, and using this data, but their information-gathering and -analysis efforts have been able to shed light on only 68 percent of the SDG indicators so far, according to a 2019 UN study.

To help fill the gap, the data science for social good (DSSG) movement has for years been making datasets about important social issues—such as health care infrastructure, school enrollment, air quality, and business registrations—available to trusted organizations or the public. Large tech companies such as Facebook, Google, Amazon, and others have recently begun to embrace the DSSG movement. Spurred on by advances in the field, the Development Data Partnership, the World Economic Forum’s 2030Vision consortium, and Data Collaboratives, they’re offering information about social media users’ mobility during COVID-19, cloud computing infrastructure to help nonprofits analyze large datasets, and other important tools and services.

But sharing data resources doesn’t mean they’ll be used effectively, if at all, to advance social impact. High-impact results require recipients of data assistance to inhabit a robust, holistic data ecosystem that includes assets like policies for safely handling data and the skills to analyze it. As tech firms become increasingly involved with using data and data science to help achieve the SDGs, it’s important that they understand the possibilities and limitations of the nonprofits and other civil society organizations they’re working with. Without a firm grasp on the data ecosystems of their partners, all the technical wizardry in the world may be for naught.

Companies must ask questions such as: What incentives or disincentives are in place for nonprofits to experiment with data science in their work? What gaps remain between what nonprofits or data scientists need and the resources funders provide? What skills must be developed? To help find answers, TechChange, an organization dedicated to using technology for social good, partnered with Project17, Facebook’s partnerships-led initiative to accelerate progress on the SDGs. Over the past six months, the team led interviews with top figures in the DSSG community from industry, academia, and the public sector. The 14 experts shared numerous insights into using data and data science to advance social good and the SDGs. Four takeaways emerged from our conversations and research:

1. Support the hidden work that makes effective data science possible.

Data scientists spend nearly half their time making sure their data is reliable, clean, and well organized. But too often, social sector organizations don’t have the resources to invest in essential steps like standardizing or digitizing data, which are critical to ensuring the quality of their data science projects.

Their struggles are understandable. Depending on the institution, preparing the “right data” for the task at hand could range from digitizing paper records to synchronizing knowledge management systems across different teams. This behind-the-scenes work can also involve the difficult task of developing and using standards for interoperability and data sharing. Some organizations are trying to make this easier. Carl Elmstam and Roderick Besseling, digital development experts from the Swedish development agency Sida and the Dutch Foreign Ministry, referenced the International Aid Transparency Initiative (IATI) to create a global standard for publishing data about humanitarian funding and projects. Such standards are important for coordinating complex, highly collaborative projects. For example, effective global research on COVID-19 requires harmonizing case data related to the disease.

The experts we spoke with said that tech companies can help civil society organizations struggling with this initial stage of a big data project by:

• Elevating awareness about the importance of organizing and standardizing data.
• Requiring funded groups to adhere to standards like those put forth by the IATI.
• Offering financial resources for social sector organizations to do this behind-the-scenes work.

2. Help partners build their data culture and digital transformation strategies.

A digital transformation strategy is an action plan to help an organization improve how it leverages digital tools and techniques, including data, to advance its goals more efficiently and effectively. By taking a big-picture view across the often deeply entrenched silos within social sector organizations, these strategies can help put the people, priorities, and incentives in place to cultivate a data culture of people who responsibly and proactively engage with data to solve problems. The global pandemic has underscored the importance of such strategies: Organizations have needed to adapt to working digitally and remotely, while nonprofits facing existential financial challenges have been forced to use data to invest limited resources where they’re most needed.

However, only 23 percent of nonprofits have a long-term vision for technology, according to a recent survey by Salesforce, and experts we spoke with shared similar observations.

“Organizations are trying to deal first with technology in itself,” said Catalina Escobar, a co-founder of the Colombian social change group MAKAIA. “They’re thinking about starting a digital transformation strategy.”

Some organizations are more advanced. The global NGO CARE developed a Responsible Data Maturity Model to help institutions craft a coherent, long-term data strategy. Partnerships such as the Digital Impact Alliance (DIAL) and New York University’s Open Data Policy Lab help coordinate successful digital and data-transformation strategies in the global development sector through frameworks like the principles for digital development.

If tech companies want their data or computing power to achieve their full potential in the DSSG movement, the experts we interviewed recommended:

• Working with partners that already have a digital transformation strategy.
• Helping organizations to design digital transformation strategies that will enable the meaningful use of data.
• Funding or contributing to efforts, such as those of CARE or DIAL, to support digital transformation at scale.

3. Provide skilled data scientists to work directly with nonprofits.

Tech companies often provide data or infrastructure for nonprofits, but too few nonprofits have the in-house experts (such as data scientists or data product managers) to make use of those digital assets. To fill this gap, some organizations, such as Zindi, put together competitions for online communities of data scientists to solve social problems. And initiatives such as Data Science Africa or Google’s AI Impact Challenge provide funding, training, and data science talent to nonprofits.

Although blueprints exist for tech companies to boost nonprofits’ data science chops, the experts we interviewed had mixed feelings about the success and sustainability of such efforts. The problem is that they can push nonprofits toward depending on tech firms for specific projects for limited periods, rather than building long-term independent capabilities.

“Anything that temporarily increases capacity at a nonprofit is not leading to more data-driven work,” said Claudia Juech, founding CEO and a board member of the Cloudera Foundation. “Once these people pull out, it will be very hard for nonprofits to keep it going and make it a part of their work.”

Some people argue we should experiment with more direct methods to strengthen in-house data capacity in the social sector. But Groups like DataKind have found success with a model they’ve developed for embedding data specialists into nonprofits to solve problems together. Their approach involves two important components: comprehensively understanding a nonprofit’s data problems and, secondly, training data scientists to appreciate the complexities, constraints, and incentives of working effectively inside nonprofits. DataKind’s senior director of product, Caitlin Augustin, and Afua Bruce, the organization’s chief program officer, said it is important to recruit the right people for such projects—they need to be highly skilled but humble, and able to communicate well with non-experts.

Nick Hamlin, a data scientist at Global Giving, agrees with the approach, further suggesting that—given many nonprofits’ struggles with limited resources—the data scientists chosen for such projects possess a “willingness to exist in the presence of uncertainty.”

To help civil society organizations’ address skill gaps in data science and to ensure tech firms’ digital contributions are used to their full potential, our experts suggested:

• Training data scientists, at scale and in a coordinated fashion across companies, on the work cultures, structures, and constraints of nonprofits.
• Continuing the funding of proven models to lend data science talent to nonprofits in longer-term engagements.
• For shorter-term engagements, deploying data scientists who are good communicators and trainers so they leave nonprofit teams with the skills to do the work on their own.

4. Support training for nonprofits to make responsible and ethical decisions based on data.

Social sector leaders need to be mindful of the potential biases that can seep into the collection, analysis, and presentation of data, or they risk taking inappropriate actions based on the information.

There are many educational and training resources tech companies can use to train their own teams and nonprofits in the critical thinking skills that ensure data projects are ethically executed. For example, the Johns Hopkins Center for Government Excellence and New York University’s GovLab provide training on interpreting data or understanding the risks when sharing data. TTC Labs, a project initiated and supported by Facebook, helps people across industries explore how to manage trust and transparency as they work with digital products and data. The global development NGO IREX helps leaders build soft skills for data-informed decision-making , while TechChange’s “Gender Data 101” course helps people be mindful of gender biases when working with data for social impact. To further bolster the creation and sharing of resources like these, our interviewees suggested that tech firms:

• Invest in training and educating their own employees on ethics and decision-making related to data.
• Sponsor training for nonprofits to build critical thinking, interpretation, and decision-making skills.
• Partner with non-traditional learning institutions that build critical thinking skills for using data ethically, and support similar efforts within the formal education system.

Avoid Going It Alone

As tech companies release datasets, donate cloud computing resources, or lend data scientists to the social sector, the experts we spoke with emphasized the importance of couching the efforts within existing systems, strategies, and knowledge. Coordination among all participants in the DSSG ecosystem—government, civil society, and private sector—will be critical, and numerous networks are already set up to do just that: 2030Vision, the Global Partnership for Sustainable Development Data, Data Collaboratives, the Data Culture Project, and others. Working through cross-industry cooperatives like these will help avoid redundant and siloed investments—and ensure we move more quickly toward achieving the SDGs as their deadlines loom.

The post How Tech Companies Can Advance Data Science for Social Good appeared first on Artificial Intelligence.

Amazon Web Services partnered with social media platform developer RallyPoint, the Department of Veterans Affairs and Harvard University to develop a machine learning model that can detect mental health issues among veterans, Nextgov reported Friday.

Under an agreement with VA, the Amazon Machine Learning Solutions Lab worked with RallyPoint and Harvard’s Nock Lab mental health professionals to utilize data science for identifying “high-value use cases” among users of the military-focused social network.

AWS provided data-labeling services as well as its SageMaker managed-service platform to train a machine-learning model to detect signs of risk in anonymous public posts on RallyPoint.

The team is accepting feedback on the effort and plans to further develop the model in the coming months. They also seek to provide RallyPoint users with access to mental health programs, support groups and hotlines, according to the report.

Dave Gowel, CEO of RallyPoint, said the effort is aimed at supporting government efforts to address veterans’ mental health issues through programs such as VA’s Suicide Prevention Program and the President’s Roadmap to End a National Tragedy of Suicide.

The team effort builds on RallyPoint’s memorandum of understanding with VA to improve veteran interactions with communities signed in 2018.

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With the rapid growth of this world, there are so many fields to choose from. It’s not just engineering or medicine, there are many more options. With so many options given, there is one particular field which is quite interesting – data science. The term sounds familiar, but many people are confused about what exactly data science is. It is the most trending field in the tech industry and is highly demanded.

To begin with, data science is a mixture of many fields and concerns with data and is a huge part of artificial intelligence. It is a mixture of software, statistics and business analysis. It mostly comes under technology, but many other skills are needed to choose it as a career. The main job of a data scientist is to load the data, structure it, and find possible solutions.

The work of a data scientist is to first collect data, which is mostly machine fed data from various sources. He then sorts them and processes them, or rather segregates the data into categories. Generally, the data collected from various sources is common for many things and needs to be segregated and cleaned. The process is similar to cleaning your room. You remove the excess and useless garbage and keep the required things for further use. The process of segregation is called data cleaning. This cleaned data is then processed and is where the statistics come into the picture. The processed data is assembled into proper algorithms for further study and analysis. After this process, the data is used to come up with solutions to solve complex problems, which helps in maximizing the profits of that particular company.

As this field is multidisciplinary, there are many aspects which it involves like mathematics, technology and business. First comes mathematics, this mostly includes statistics and algebra and much more. Many people think that data science is only about statistics and that is false. Statistics is an important part of data science, but not the only thing required. Linear algebra is also important as it uses the algorithms used to process the data.

Next is technical knowledge, such as coding and hacking. It is a vital skill for a data scientist as most of the time he is in contact with data which can be processed by this very skill. To process this data, a data scientist must know his technical stuff as the data provided is enormous and dealing with the data is a tedious process where having great technical skills is a must. When it comes to hacking, it does not mean the word ‘hacking’ that we all have heard about like breaking into computers. It means the creativity in using those technical skills. This is the key quality a good data scientist must have. Python, R, SQL etc are mostly the coding languages used for data science.

And the last comes business acumen, which is also a vital skill for a good data scientist. The data processed needs to be used to solve complex problems to maximize profits. Mostly these problems are core business problems. Therefore, having a good business tactic is important.

Since data science is an emerging field, it has a huge demand. If you have all the above skills, data design is meant for you.

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Or maybe it is just the latest example of the hype getting way ahead of the reality? On this week’s Tech Tent we find out what the big fuss is about something called GPT-3.

OpenAI is a Californian company started in 2015 with a high-minded mission – to ensure that artificial general intelligence systems that could outperform humans in most jobs would benefit all humanity.

It was founded as a non-profit with generous donations from Elon Musk among others but was quickly transformed into a for-profit business, with Microsoft investing $1bn.

Now it has released GPT-3, a product which has had social media, or that part of it which obsesses over new technology, buzzing with excitement in recent days.

It is an AI, or to be more precise, machine-learning – tool that seems to have amazing capabilities. In essence, it is a text generator but users are finding it can do everything from writing an essay about Twitter in the style of Jerome K Jerome, to answering medical questions or even coding software programs.

So far, it has only been available to a few people who asked to join the private beta, among them Michael Tefula. He works for a London-based venture capital fund but describes himself as a technology enthusiast rather than a developer or computer scientist.

He explains that what makes GPT-3 so powerful is the sheer volume of data it has ingested from the web when compared to an earlier version of the program. “This thing is a beast in terms of how much better it is compared to GPT-2.”

So what can you do with it?

“It’s really down to how creative you are with the tool, you can basically give it a prompt of what you would like it to do. And it will be able to generate outputs based on that prompt.”

Michael decided to see how well it would perform in taking complex legal documents and translating them into something comprehensible

“I gave it two or three paragraphs that came from a legal document.

“And I also gave it two or three examples of how a simplified version of those paragraphs would look.”

Having been trained, GPT-3 was then able to provide simplified versions of other documents.

He went on to see whether it could learn his writing style and generate emails that would sound like him – and the results again were impressive.

Which brings us to one of the problems with this technology. Last year OpenAI, apparently remembering its mission to protect humanity, said it would not release a full version of GPT-2 because that would raise safety and security concerns.

In an era of fakery, an algorithm that could generate articles that might sound like a prominent politician could prove dangerous.

Why then, asked some critics, was the more powerful GPT-3 any different? Among them was Facebook’s head of AI Jerome Pesenti who tweeted: “I don’t understand how we went from gpt2 being too big a threat to humanity to be released openly to gpt3 being ready to tweet, support customers or execute shell commands.”

He raised the issue of the algorithm generating toxic language reflecting the biases in the data on which it has been fed, pointing out that when given words such as “Jew” or “women” it generated anti-Semitic or misogynistic tweets.

OpenAI’s founder Sam Altman seemed keen to calm those fears, tweeting: “We share your concern about bias and safety in language models, and it’s a big part of why we’re starting off with a beta and have a safety review before apps can go live.”

But the other question is whether, far from being a threat to humanity, GPT-3 is anything like as clever as it appears. The computer scientist who heads Oxford University’s artificial intelligence research, Michael Wooldridge, is sceptical.

He told me that while the technical achievement was impressive, it was clear that GPT-3 did not understand what it was doing, so talk of it rivalling human intelligence was fanciful: “It is an interesting technical advance, and will be used to do some very interesting things, but it doesn’t represent an advance toward general AI. Human intelligence is much, much more than a pile of data and a big neural net.”

That may be the case, but I am still eager to give it a try. Look out over the coming weeks for evidence of blogs or radio scripts written by a robot.

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