What’s new in the world of artificial intelligence?

Artificial intelligence is changing the course of our lives with its constant developments. Before the pandemic and now in the new normal, AI remains to be a key trend in the tech industry. It is reaching wider audiences as years pass and scientists, engineers, and entrepreneurs who involve themselves with modern technologies are reaping the benefits of AI and its branches, IoT and machine learning.

Organizations that overlooked digital transformation and the power of artificial intelligence are picking the pace of AI adoption. When COVID-19 was creating chaos across industries, it became evident that disruptive technologies and the automation that comes with it are more than crucial.

While 2020 was a great year for artificial intelligence working with its true potential, here are the latest advancements in the field of AI that are promising exciting times for the future of this technology.

1. An AI algorithm that can predict virus spread from animals to humans

Researchers from the University of Gothenburg have found an artificial intelligence model to predict what kind of virus can possibly spread from animals to humans. Using artificial intelligence, the algorithm studies the role of carbohydrates to understand the infection path. In scientific terms, carbohydrates are called glycans and they play a significant role in the way our bodies function. Almost all viruses first affect the glycans in our bodies, so did the coronavirus. Led by Daniel Bojar, assistant professor at the University of Gothenburg, the new AI model can analyze glycans with improved accuracy. The model analyses the infection process by predicting new virus-to-glycan interactions to better understand zoonotic diseases.

2. Another AI algorithm that can outsmart hackers with fake documents

The world is evolving with disruptive technologies and that includes hackers and cyber attackers. Cyberattacks have become more common amidst the remote working culture, where sensitive files and documents have become the prime targets. To deal with this pressing concern, V.S.Subrahmanian, a cybersecurity researcher at  Dartmouth College, created an algorithm called Word Embedding-based Fake Online Repository Generation Engine (WE-FORGE) that generates fake patents that are under development. This makes it difficult for hackers to find what they are looking for. The system generates convincing fakes based on the keywords of a given document. For each keyword it identifies, it analyses a list of related topics and replaces the original keyword with one randomly chosen related word.

3. DataRobot to launch a new enterprise AI platform to leverage business and customer’s value

DataRobot announced its second major platform launch, DataRobot version 7.1 with new MLOps management agents, time series model enhancements, and automated AI reports. With an aim to provide lifecycle management for remote AI and machine learning models, DataRobot’s new launch will offer feature discovery push-down integration for Snowflake and time series Eureqa model improvements. Through this, Snowflake users can use automatic discovery and computation of individual independent variables in the Snowflake data cloud. Apart from these additions, DataRobot also provides a no-code app builder that has the ability to convert deployed models into AI apps with no coding.

4. Exscientia is leveraging AI-powered drug discovery with Allcyte’s deep learning platform

Exscientia’s US$60M acquisition of Allcyte will boost AI-drug discovery. Allcyte is an Austrian company that is developing an artificial intelligence platform to study how cancer treatments work on different individuals. Post the acquisition, this technology will work with Exscientia’s native software that uses AI to identify potential drug targets, build the right drugs, and send them for trials. Exscientia will now be able to work with a precision medicine approach to design drug molecules, ensuring improved efficiency.

5. As a relief for asset managers, Redi2 and IBM Private Cloud Services pioneering a new solution

The creator of the SaaS delivery model for financial services billing solutions, Redi2 Technologies, has announced a collaboration with IBM Private Cloud Services to improve flexibility. The combination of these technologies will add strong value for Redi2 Revenue Manager clients. Top asset managers throughout the world can take advantage of the improvements like fast reaction options for clients who need quick responses to changes, move data from one country to another or expand their infrastructure requirements.

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The report, titled Global Data Science and Machine Learning Service market defines and briefs readers about its products, applications, and specifications. The research lists key companies operating in the global market and also highlights the key changing trends adopted by the companies to maintain their dominance. By using SWOT analysis and Porter’s five force analysis tools, the strengths, weaknesses, opportunities, and threats of key companies are all mentioned in the report. All leading players in this global market are profiled with details such as product types, business overview, sales, manufacturing base, competitors, applications, and specifications.

Various factors are responsible behind the market’s growth trail, which are studied at length in the report. In addition, the report lists down the restraints that are posing threat to the Data Science and Machine Learning Service market.

Different global regions such as North America, Latin America, Asia-Pacific, Europe, and India have been analyzed on the basis of the manufacturing base, productivity, and profit margin. This Data Science and Machine Learning Service market research report has been scrutinized on the basis of different practical oriented case studies from various industry experts and policymakers. It uses numerous graphical presentation techniques such as tables, charts, graphs, pictures and flowchart for easy and better understanding to the readers.

Different internal and external factors such as, Data Science and Machine Learning Service Market have been elaborated which are responsible for driving or restraining the progress of the companies. To discover the global opportunities different methodologies have been included to increase customers rapidly.

The report’s conclusion leads into the overall scope of the global market with respect to feasibility of investments in various segments of the market, along with a descriptive passage that outlines the feasibility of new projects that might succeed in the global Data Science and Machine Learning Service market in the near future.

Key questions answered in the report include:

• What will be the market size and the growth rate by the end of the forecast period?
• What are the key Data Science and Machine Learning Service Market trends impacting the growth of the market?
• What are the potential growth opportunities and threats faced by the leading competitors in the market?
• What are the key outcomes of Porter’s five forces analysis and the SWOT analysis of the key players functioning in the global Data Science and Machine Learning Service Market?
• This report gives all the information regarding industry Overview, analysis and revenue of this market.
• What are the market opportunities and threats faced by the vendors in the global Data Science and Machine Learning Service market?

Table of Content:

Global Data Science and Machine Learning Service Market Research Report 2020-2026

Chapter 1: Industry Overview

Chapter 2: Data Science and Machine Learning Service Market International and China Market Analysis

Chapter 3: Analysis of Revenue by Classifications

Chapter 4: Analysis of Revenue by Regions and Applications

Chapter 5: Analysis of Data Science and Machine Learning Service Market Revenue Market Status.

Chapter 6: Sales Price and Gross Margin Analysis

………Continue for TOC……..

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International Business Machines Corporation IBM recently updated Watson Studio with AutoAI capabilities.

As the name suggests, AutoAI enables enterprises to automate complex arduous processes, comprising optimizing, designing and other related tasks by leveraging robust AI capabilities.

Markedly, innovation in machine learning (ML) and improvement in data set quality drives development of AI tools. IBM rolled out AutoAI with an aim to free up time for data engineers to design sturdy ML models to expedite AI deployment.

AutoAI capabilities function in sync with Watson Machine Learning to accelerate AI processes considerably. AutoAI is available in “Watson Studio on the IBM Cloud.”

IBM is leaving no stone unturned to strategically infuse AI capabilities across its offerings, which hold promise. AutoAI is anticipated to boost adoption of IBM Watson Studio services, consequently bolstering the company’s top line in the days ahead.

Noteworthy Features

With AutoAI, data scientists can utilize “hyperparameter optimization capabilities” which facilitates development of AI models. Moreover, AutoAI is loaded with robust enterprise data science models, including gradient boosted trees which allow users to upscale ML deployment processes.

In a bid to empower users with deep learning (DL) capabilities, AutoAI allows access to IBM Neural Networks Synthesis (NeuNetS). Notably, NeuNetS utilizes AI to aid development of DL model and automates data synthesis across custom-designed neural networks. Further, NeuNetS provides users with the flexibility to prioritize accuracy or speed of the data model.

Increasing Spend on AI Favors Growth Prospects

Huge volume of data is being generated through a wide range of sources. In a bid to harness this data constructively, the companies intend to leverage AI to provide specific solutions and enhance solutions’ capabilities. However, deployment of AI tools is not trouble free.

For instance, per an IBM study, Shifting Toward Enterprise-Grade AI , conducted last year, 63% of surveyed individuals cited “lack of proper technical skills was a prime challenge to AI implementations.”

Moreover, according to a Forrester study, 60% of surveyed candidates claimed management of data quality to be one of the headwinds in delivering AI applications.

Markedly, IBM’s new Watson AutoAI capabilities provide companies with robust data models, consequently accelerating their AI processes.

We must note that the number of companies utilizing enterprise AI is increasing, which favors the prospects of AutoAI tool in Watson Studio. Per a report by IBM, customer satisfaction is the primary reason behind around 77% of organizations focusing on AI investments.

Further, IDC data projects worldwide spending on AI systems to hit $79.2 billion by 2022 at a CAGR of 38% from 2018 to 2022.

The move is expected to strengthen the company’s position in machine learning as a service (MLaaS) market, which is envisioned to hit $8.31 billion by 2023 per a MarketReportsWorld report, as revealed by MarketWatch.

The aforementioned secular trends reinforce IBM’s growth prospects in the longer haul.

Competition a Woe

Rising competition from Amazon Web Services (AWS) and Microsoft Azure, which dominate the cloud infrastructure services market, is a headwind. Notably, the cloud players are strengthening their IT infrastructure with advanced ML capabilities. For instance, Microsoft recently rolled out additional functionalities to Azure Machine Learning.

According to latest first quarter data from Synergy Research, IBM Cloud is classified as “strong niche player” with comparatively “lower growth rates.”

Zacks Rank & Key Picks

IBM carries a Zacks Rank #3 (Hold).

Some better-ranked stocks worth considering in the broader sector are eGain Corporation EGAN , Rosetta Stone Inc. RST and j2 Global, Inc. JCOM , each sporting Zacks Rank #1 (Strong Buy). You can see the complete list of today’s Zacks #1 Rank stocks here .

Long-term earnings growth rate for eGain, Rosetta Stone and j2 Global is pegged at 30%, 12.5% and 8%, respectively.

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